WiSE Power + Active Suspension Control

Leveraging Edge LLMs, Human-in-the-Loop Interaction, Game Theory, and Topological Signal Processing

Co-created by the Catalyzer Think Tank divergent thinking and Gemini Deep Research tool.

Abstract

This report details a novel, integrated framework for advanced vehicle active suspension control, aiming to transcend the limitations of conventional approaches by synergistically combining Edge Intelligence Large Language Models (LLMs), Human-in-the-Loop (HITL) interaction, Game Theory optimization, and Topological Data Analysis (TDA) including Homotopy Theory and Manifold Learning concepts. The increasing demand for superior ride comfort, handling stability, and energy efficiency, particularly in electric and automated vehicles, necessitates control systems capable of adapting to complex, dynamic environments and user preferences. This framework proposes utilizing edge-optimized LLMs within the vehicle control unit (VCU) for semantic comprehension of sensor data (processed via TDA), contextual understanding, and high-level decision-making. Human interaction is incorporated via HITL principles to provide preferences, validation, and oversight, enhancing adaptability and trust. Game theory, specifically potential games and Nash equilibrium seeking, provides a rigorous mathematical structure for optimizing suspension actuator commands, explicitly balancing conflicting objectives like comfort, handling, and energy consumption based on inputs from the LLM and human. TDA techniques are employed for advanced signal processing of suspension sensor data, extracting robust topological and geometric features that capture underlying system states and anomalies beyond traditional methods. The report comprehensively defines these core concepts, elaborates the rationale for their integration, details implementation methodologies for adapting LLMs for edge deployment, designing the HITL interface, formulating the game-theoretic control problem, applying TDA to sensor signals, and outlines the system integration process. Finally, it critically evaluates the significant challenges inherent in developing and validating such a complex system, including computational constraints, real-time performance, safety assurance, and the intricacies of merging diverse theoretical paradigms.

Introduction

The pursuit of enhanced vehicle dynamics, encompassing ride comfort, handling stability, and overall safety, drives continuous innovation in automotive control systems. Active suspension systems, which dynamically adjust vehicle posture and response to road conditions, represent a significant advancement over traditional passive or semi-active systems.1 However, designing controllers for these systems that perform optimally across the wide spectrum of real-world driving scenarios – encompassing varying road surfaces, vehicle speeds, loads, and driver intentions – remains a formidable challenge.1 Conventional control strategies often struggle to adapt effectively to highly dynamic and uncertain conditions, particularly with the added complexity of multi-power systems found in electric vehicles (EVs) where energy management is paramount, and the increasing prevalence of automated driving features.1

This report introduces and analyzes a novel, integrated framework designed to address these challenges by leveraging a confluence of cutting-edge technologies from artificial intelligence (AI), control theory, human-computer interaction (HCI), and applied mathematics. The proposed architecture centers on an Edge Intelligence Large Language Model (LLM) deployed within the vehicle control unit (VCU). This edge LLM operates within a Human-in-the-Loop (HITL) paradigm, allowing for dynamic interaction with the driver. Control decisions are optimized using Game Theory maximization principles, informed by the LLM’s contextual understanding and human input. Furthermore, sophisticated signal processing, drawing on Homotopy Theory, 3-Manifold concepts (primarily through the lens of Topological Data Analysis – TDA), and Manifold Learning, is employed for comprehension, decision support, and state projection based on sensor data from a multi-power, fully active suspension system.

The primary objective of this report is to provide a comprehensive technical exposition of this integrated framework. It aims to meticulously define the core concepts involved, articulate the compelling rationale (‘why’) for their synergistic combination, detail the specific methodologies (‘how’) required for the implementation of each constituent component and their integration into a cohesive system, and critically assess the inherent challenges and potential limitations associated with its development and deployment. The analysis targets an audience of advanced technical experts, including researchers and senior engineers engaged in automotive research and development, AI, and control systems engineering.

The report is structured as follows: Section 1 defines the foundational technologies: Edge LLMs, HITL systems, Game Theory maximization, TDA/Homotopy/Manifold Learning, and Multi-Power Active Suspension systems. Section 2 explores the rationale for integrating these diverse technologies, highlighting the potential synergies and advantages over conventional methods. Section 3 delves into the specific implementation strategies for adapting the Edge LLM, designing the HITL interface, formulating the game-theoretic control problem, and applying topological analysis to suspension signals. Section 4 outlines the system integration process, detailing the data flow architecture and component interactions. Section 5 provides a critical discussion of the challenges, limitations, and potential future research directions. Finally, the conclusion summarizes the key findings and offers a perspective on the feasibility and impact of this advanced control framework.

Section 1: Foundational Concepts: Definition and Explanation

This section provides a detailed definition and explanation of the core technological and theoretical concepts underpinning the proposed integrated active suspension control framework.

1.1 Edge Intelligence LLMs for Vehicle Control Units (VCUs)

Definition: Edge Intelligence (EI) signifies a paradigm shift in data processing, moving computation away from centralized cloud servers towards edge devices located closer to the data source.7 In the automotive context, the Vehicle Control Unit (VCU) serves as a primary edge device. EI aims to reduce latency, minimize bandwidth requirements, enhance privacy and security by processing data locally, and enable rapid, independent decision-making.7 Edge Large Language Models (Edge LLMs) are sophisticated AI models, specifically LLMs, that have been adapted and optimized for deployment and execution on such resource-constrained edge devices.8 These models, characterized by their large number of parameters and training on extensive datasets, possess advanced capabilities in understanding and generating human language, and increasingly, in processing multimodal information and exhibiting reasoning abilities.7

Characteristics: For effective deployment on VCUs, Edge LLMs must possess specific characteristics tailored to the demanding automotive environment:

• Low Latency: Real-time vehicle control necessitates extremely fast inference speeds to react to dynamic changes in milliseconds.7
• Reduced Model Size: VCUs have limited memory capacity compared to cloud servers, requiring significant model compression.8
• Low Power Consumption: Energy efficiency is critical, especially in EVs where computational loads directly impact vehicle range.5 Active suspension itself consumes power, making efficient control algorithms crucial.5
• Sufficient Processing Capability: Despite resource constraints, the Edge LLM must be capable of performing complex tasks like interpreting sensor data, understanding context, and making informed decisions.7
• Multimodal Input Processing: Automotive applications often require processing diverse data types, including text (e.g., driver commands, diagnostic messages), images (e.g., from cameras for scene understanding), and numerical sensor data (e.g., from suspension sensors, IMUs).10 Edge LLMs are increasingly designed to handle such multimodal inputs.10
• Reasoning and Generalization: LLMs offer capabilities beyond simple pattern matching, including reasoning, context understanding, and generalization to novel situations, paving the way towards Edge General Intelligence (EGI) where edge devices can autonomously reason, learn, and adapt.7

Relevance: Within the proposed framework, Edge LLMs serve as the central intelligence hub. They are envisioned to interpret complex patterns identified by the TDA module from sensor signals, understand driver preferences or commands communicated via the HITL interface, integrate contextual information (e.g., road type, weather, traffic density), make high-level, context-aware decisions about control objectives (e.g., prioritizing comfort vs. handling), and potentially project future vehicle states or road conditions to enable proactive control adjustments.11

The deployment of LLMs onto VCUs represents a significant evolution from traditional embedded controllers. Whereas conventional units execute pre-defined, deterministic algorithms based primarily on numerical inputs, edge LLMs introduce capabilities for advanced reasoning, semantic understanding of context, and potentially adaptation based on multimodal inputs.7 This transforms the VCU from a mere executor of fixed logic into a localized intelligent agent capable of interpreting richer, more nuanced information – including semantic concepts 15 – beyond raw numerical signals. This shift enables the development of more sophisticated, context-aware, and adaptive control strategies that can better handle the complexities of real-world driving.

1.2 Human-in-the-Loop (HITL) Systems for Real-Time Control

Definition: Human-in-the-Loop (HITL) systems are designed to integrate human operators directly into the operational loop of an automated or semi-automated system.21 This paradigm leverages the complementary strengths of humans (e.g., judgment, intuition, contextual understanding, adaptability, ethical reasoning) and machines (e.g., speed, precision, data processing capacity).23 HITL is particularly relevant for complex, dynamic, and safety-critical systems, such as vehicle control, where achieving full, reliable autonomy is challenging or where human oversight and intervention are desired or necessary.21

Roles: Within a HITL control system, humans can assume various roles depending on the system design and operational context 21:

• Sensor: Providing qualitative assessments or unique information unavailable to system sensors (e.g., noticing unusual road conditions, assessing subjective ride feel).21
• Actuator: Manually initiating or executing adaptation actions based on system recommendations or personal judgment.21
• Decision-Maker/Validator: Setting high-level goals, guiding the AI’s decision process, resolving ambiguities, correcting errors, validating system outputs, or taking over control in critical situations.21 This includes different levels of engagement, such as human-in-the-loop (active input required for operation) versus human-on-the-loop (monitoring and intervening when necessary).24
• Fallback Mechanism: Serving as a safety backup in case of system failure or encountering situations beyond the system’s designed capabilities.21

Interface: The effectiveness of a HITL system hinges critically on the design of the Human-Computer Interface (HCI), also referred to as Human-Machine Interface (HMI) or Computer-Human Interaction (CHI).30 The interface must facilitate seamless and intuitive communication, ensuring the human can understand the system’s state and reasoning (transparency) and can provide input effectively and efficiently.25 Interface design must account for human factors such as attention span, cognitive workload, reaction time, potential for error, and the need for trust in the system.21

Relevance: In the context of active suspension control, a HITL approach allows the system to be personalized and more robust. Drivers could use the interface to specify their preferences (e.g., a desired balance between comfort and handling), provide feedback on the perceived ride quality, or validate the system’s behavior in unusual or complex scenarios.25 This collaboration can enhance safety by keeping the human engaged, improve user acceptance by tailoring performance, and increase robustness by leveraging human judgment in situations where the AI might be uncertain.22

The conception of HITL within this advanced framework likely extends beyond a simple manual override capability. It suggests a more dynamic and potentially bidirectional partnership.25 Human input, potentially semantic in nature (“make the ride smoother on this bumpy road”), could dynamically influence the LLM’s interpretation of the situation or adjust the objectives within the game-theoretic optimization.29 Conversely, the system could provide informative feedback to the human about its state, intentions, or the reasons behind its actions, fostering trust and enabling more effective collaboration.24 This continuous interaction loop allows for refinement and adaptation based on shared control and mutual understanding, rather than just providing a safety net.25

1.3 Game Theory Maximization in Dynamic Systems

Definition: Game theory is a branch of mathematics focused on modeling and analyzing strategic interactions among rational decision-makers, termed “players,” whose outcomes are interdependent.34 Each player chooses a strategy to maximize their own utility or payoff, considering the potential strategies of other players.35 In the context of control engineering, game theory provides a powerful framework for formulating and solving optimization problems in dynamic systems, especially those involving multiple agents or interactions with uncertain environments or disturbances.34 “Maximization” refers to the core objective of finding strategies that optimize a predefined payoff function representing desired system performance criteria, such as ride comfort, handling stability, or energy efficiency.14

Potential Games: A particularly useful class of games in control applications is potential games.14 In a potential game, there exists a global function, the potential function, such that the change in any single player’s individual utility resulting from a unilateral change in their strategy is exactly mirrored by the change in the potential function.14 This property elegantly links individual, decentralized optimization efforts to a global system objective. When players employ learning algorithms or strategy update rules to maximize their own payoffs, the system converges towards a Nash equilibrium.14 A Nash equilibrium is a stable state where no player can improve their outcome by changing their strategy alone, given the strategies of the other players.35 In potential games, Nash equilibria correspond to local optima of the potential function.34

Application: Game theory can be applied to active suspension control in several ways. One common formulation models the interaction as a game between the suspension controller (Player 1) and road disturbances (Player 2).39 The controller seeks to maximize performance (e.g., comfort, stability), while disturbances act to degrade it. This often leads to zero-sum game formulations or robust control frameworks like H∞ control, which can be cast as differential games handling interactions over time.35 Alternatively, game theory can coordinate the actions of multiple actuators (e.g., one per wheel) or integrate suspension control with other active chassis systems (like steering or braking), modeling them as cooperative or non-cooperative players seeking a collective or individual optimum.14 Nash equilibrium seeking strategies are particularly relevant for such multi-agent coordination problems.33

Relevance: For active suspension control, game theory offers a rigorous mathematical framework to address the inherent trade-offs between conflicting performance objectives, most notably ride comfort versus handling stability.3 By defining appropriate payoff functions that encapsulate these objectives and potentially incorporate factors like energy consumption and actuator limitations, game theory allows for the computation of optimal control strategies that represent the best possible compromise under the current dynamic conditions and uncertainties.

The application of game theory, especially through concepts like potential games or Nash equilibrium seeking 14, provides an intrinsic mechanism for managing the conflict between ride comfort and handling stability. In traditional suspension design, these objectives are often viewed as a static trade-off, requiring a fixed compromise.3 Game theory, however, allows this conflict to be explicitly encoded within the payoff functions. The process of finding an equilibrium solution then dynamically determines the optimal balance based on the current state, disturbances, and potentially high-level goals provided by the LLM or human user. This moves beyond simple pre-programmed switching logic or fixed compromises, enabling a continuously optimized response.

1.4 Homotopy Theory, 3-Manifolds, and Topological Data Analysis (TDA) in Signal Processing

Homotopy Theory: Homotopy theory is a fundamental branch of algebraic topology concerned with the study of continuous deformations of topological spaces and paths within them.45 It provides methods for classifying spaces based on properties that are invariant under such deformations. Key concepts include homotopy equivalence, which defines when two spaces can be continuously transformed into one another without tearing or gluing, providing a flexible notion of topological “sameness”.45 Homotopy groups, denoted πn​(X), are algebraic invariants that capture information about the connectivity and the presence of n-dimensional “holes” in a space X.45 A variant known as A-homotopy theory has been developed specifically for analyzing the combinatorial structure of discrete objects like graphs and simplicial complexes, diverging from classical homotopy by focusing on combinatorial connectivity rather than topological properties.48

3-Manifolds: A 3-manifold is a topological space in which every point has a local neighborhood that is topologically equivalent to an open ball in 3-dimensional Euclidean space (R3). The study of 3-manifolds is a major area within topology, focusing on their classification and geometric structures. In the context of this report, 3-manifolds are mentioned as a potential tool, likely for representing complex, multi-dimensional state spaces or data structures that might arise from analyzing time-series sensor data from the suspension system. However, the primary applicable techniques discussed in the source material fall under the broader umbrella of Topological Data Analysis (TDA).

Topological Data Analysis (TDA): TDA is a relatively recent field that applies concepts and tools from algebraic topology, including homotopy, to analyze the large-scale structure or “shape” of complex and often high-dimensional datasets.45 It assumes that data points, often viewed as a point cloud in some feature space, may possess underlying geometric or topological structures that are not easily captured by traditional statistical or machine learning methods.49 Key TDA techniques include:

• Persistent Homology (PH): This is a central tool in TDA that tracks the appearance (birth) and disappearance (death) of topological features – such as connected components (H0​, related to Betti number β0​), loops or tunnels (H1​, β1​), and voids or cavities (H2​, β2​, etc.) 49 – as a scale parameter varies.45 By focusing on features that persist over a significant range of scales, PH can distinguish robust structural characteristics from noise or sampling artifacts.52 Results are often summarized in persistence diagrams or barcodes.52 PH is particularly useful for analyzing time series data, detecting changes, and identifying periodicities or anomalies.52
• Mapper Algorithm: This technique provides a way to visualize high-dimensional data by constructing a simplified combinatorial representation, typically a graph or simplicial complex.45 It involves mapping the data to a lower-dimensional space, covering this space with overlapping bins, clustering the data points within each bin, and representing the clusters and their intersections as a network. This reveals the connectivity and topological structure of the original dataset.45
• Manifold Learning: This subfield of dimensionality reduction assumes that high-dimensional data points lie on or near a lower-dimensional, possibly curved, manifold embedded within the higher-dimensional space.55 Algorithms like Isomap, Locally Linear Embedding (LLE), Hessian Eigenmapping (HLLE), Local Tangent Space Alignment (LTSA), and t-distributed Stochastic Neighbor Embedding (t-SNE) attempt to uncover this underlying manifold structure and create a low-dimensional embedding that preserves certain geometric properties (e.g., geodesic distances in Isomap, local linear relationships in LLE).56 This can be used for visualization, feature extraction, or simplifying subsequent analysis.55

Relevance: For the active suspension system, sensor signals (particularly vibration data 60) can be complex, high-dimensional, noisy, and exhibit non-linear behavior.39 TDA, homotopy concepts, and manifold learning offer powerful tools to analyze these signals.52 They can extract robust topological or geometric features that characterize the underlying state of the suspension system, identify different operating regimes (e.g., varying road roughness), detect anomalies indicative of potential faults 52, or provide a meaningful low-dimensional representation of the system’s dynamics.58 These methods move beyond traditional signal processing techniques like Fourier analysis (FFT) 62 or wavelet transforms 63, which primarily focus on frequency content, by analyzing the inherent shape, connectivity, and geometric structure of the data in abstract state spaces.49 The resulting topological features could serve as richer, more informative inputs for the LLM’s comprehension task or the game theory module’s decision-making process.

The application of TDA and related topological methods to suspension signals represents a significant departure from conventional analysis focused on time-domain statistics or frequency-domain representations.62 By concentrating on the intrinsic ‘shape’ and connectivity of the data as it evolves in a state space (often reconstructed using techniques like time-delay embedding 50), TDA can potentially uncover non-linear dynamics, multi-scale structures, critical transitions, or subtle regime shifts that might be obscured in standard signal representations.52 This focus on topology provides robustness to noise and certain types of deformation 52, making it well-suited for analyzing real-world sensor data. It offers a complementary, and potentially more insightful, way to characterize the system’s behavior, especially when dealing with complex, non-stationary dynamics or identifying early indicators of changing conditions or faults.52

1.5 Multi-Power Fully Active Suspension Systems: Dynamics and Control Objectives

Definition: Vehicle suspension systems mediate the interaction between the vehicle body and the road, aiming to isolate occupants from road irregularities while maintaining tire contact for stability and control.2 Fully active suspension systems (ASSs) differ from passive systems (fixed springs and dampers) and semi-active systems (variable dampers) by employing actuators (e.g., hydraulic cylinders, electromagnetic motors) capable of actively generating forces to control the vertical motion of the wheels and vehicle body.1 The term “multi-power” in this context likely refers to the complexities of power management in such systems, especially within EVs. This could involve drawing power from multiple sources (e.g., the main high-voltage traction battery, potentially a separate 48V system, or energy recuperated from suspension movement itself) and managing the power distribution and consumption across multiple actuators, often one per wheel.6 Efficient power management is a critical design consideration.5

Dynamics: The dynamic behavior of vehicles equipped with active suspension is typically analyzed using mathematical models of varying complexity. Common models include:

• Quarter-car models: Simplest representation, focusing on one wheel and its associated sprung mass. Often used for initial controller design and analysis.2
• Half-car models: Representing either the front/rear axle pair or the left/right side, capturing pitch or roll dynamics, respectively.40
• Full-vehicle models: More comprehensive representations, often with 7 degrees of freedom (DOF) (vertical, pitch, roll for the body; vertical for each wheel) 60 or 8-DOF (adding driver’s seat dynamics) 1, capturing coupled vertical, pitch, and roll motions. These models incorporate parameters representing sprung mass (vehicle body), unsprung masses (wheels, axles), tire stiffness and damping, suspension spring/damper characteristics, and crucially, the dynamics and force/torque capabilities of the active actuators.1 Non-linearities arising from suspension geometry, damper characteristics, tire behavior, or actuator saturation are often significant and need consideration in control design.39

Control Objectives: The primary goals of active suspension control often conflict, requiring careful balancing 3:

• Ride Comfort: This is typically the main objective, aiming to isolate passengers from road-induced vibrations. It is quantified by minimizing the vertical acceleration of the vehicle body (sprung mass), as well as pitch and roll accelerations.1 Root Mean Square (RMS) acceleration values are common metrics.1
• Handling and Stability: This involves ensuring the vehicle responds predictably and safely to driver inputs and external disturbances. Key aspects include maintaining good road holding (minimizing variations in dynamic tire load to ensure consistent grip), limiting body roll during cornering and pitch during acceleration/braking, and preventing excessive suspension travel (deflection) that could lead to hitting the bump stops.2
• Energy Efficiency: Active systems consume power, which is a significant concern, particularly for EVs where it impacts range.5 Minimizing the energy consumed by the actuators is therefore an important objective, potentially involving energy recuperation strategies where possible.6
• Actuator Constraints: Control strategies must respect the physical limitations of the actuators, such as maximum force output and maximum rate of change (slew rate).3

Challenges: Despite their potential benefits, fully active suspension systems face significant practical challenges, including high system cost, increased complexity (requiring sensors, actuators, control units), added weight, and substantial power consumption.5 Integrating active suspension with other active chassis systems (e.g., active steering, electronic stability control) within an Integrated Vehicle Dynamics Control (IVDC) framework presents further coordination challenges to avoid conflicts and maximize overall performance.4

The explicit mention of “multi-power” systems and the inherent energy constraints of EVs 5 suggests that energy management is elevated from a mere constraint to a primary control objective. It must be actively considered and optimized alongside the traditional goals of comfort and handling. This implies that the control framework (whether LLM-driven goal setting or game-theoretic optimization) needs to incorporate energy consumption and potential regeneration directly into its decision-making calculus.14 The system might dynamically adjust the trade-off between performance (comfort/handling) and energy use based on factors like battery state-of-charge, driving mode, or predicted energy demands, moving beyond static compromises.

Section 2: Rationale for Technology Integration (‘Why’)

This section elaborates on the motivation for combining Edge LLMs, HITL, Game Theory, and TDA/Homotopy/Manifold Learning into a unified framework for active suspension control. The synergy between these diverse technologies holds the potential to overcome the limitations of conventional approaches and achieve unprecedented levels of performance, adaptability, and robustness.

2.1 Synergistic Potential for Enhanced Suspension Performance

The proposed integration aims to create a control system whose capabilities exceed the sum of its individual parts, addressing the shortcomings of existing control strategies. Traditional methods like PID control can lack robustness 1, LQR performance can degrade with model uncertainty 1, Sliding Mode Control (SMC) can be sensitive to noise 1, Model Predictive Control (MPC) can be computationally expensive and model-dependent 1, and Fuzzy Logic Controllers (FLC) require significant expert tuning.1 The integrated framework seeks to mitigate these issues through a multi-faceted approach:

• LLM for Semantic Comprehension and Contextual Awareness: Edge LLMs provide the ability to process and “understand” complex information far beyond the numerical data typically handled by controllers.11 They can interpret the nuanced features extracted by TDA from sensor signals, understand natural language commands or preferences from the human driver via the HITL interface 15, and integrate diverse contextual information (e.g., map data indicating road type, weather conditions, traffic density inferred from sensors or V2X communication). This deep comprehension allows the LLM to establish more informed and contextually relevant high-level goals for the suspension system.17
• Game Theory for Rigorous Optimization: Game theory provides the mathematical engine for optimal decision-making under conflict and uncertainty.14 Based on the high-level goals and contextual understanding provided by the LLM, and incorporating constraints and preferences from the HITL system, the game-theoretic module can compute optimal actuator commands. It explicitly formulates and solves the trade-off problem between conflicting objectives like ride comfort, handling stability, and energy consumption, aiming for a Nash equilibrium or minimax solution that represents the best achievable balance in the current situation.3
• TDA for Robust State Representation: TDA and manifold learning techniques offer a powerful means of extracting meaningful information from potentially noisy, high-dimensional, and non-linear suspension sensor data.49 By focusing on the intrinsic topological and geometric structure of the data, TDA can identify robust features corresponding to different road conditions, vehicle dynamic states, or incipient faults, which might be missed by traditional signal processing methods.45 This provides a richer, more reliable state representation to inform the LLM’s comprehension and the game theory module’s calculations.
• HITL for Adaptability, Personalization, and Trust: Integrating the human element ensures the system remains adaptable to unforeseen circumstances or situations requiring subjective judgment.21 It allows for personalization according to driver preferences and driving style.29 Furthermore, providing transparency and allowing human oversight and intervention fosters user trust and acceptance, which is crucial for safety-critical systems.21 Human feedback can also be used for continuous system refinement and validation.68

2.2 Addressing Limitations of Conventional Control Paradigms

The integrated framework directly targets key limitations of conventional active suspension control approaches:

• Enhanced Adaptability: Many traditional controllers rely on fixed parameters or adapt based on relatively simple scheduling variables (e.g., vehicle speed) or limited state feedback.3 The proposed system aims for a much deeper level of adaptation. The LLM enables adaptation based on semantic understanding of the complete driving context.15 HITL allows adaptation based on explicit human intent or implicit preferences inferred from behavior.27 TDA provides adaptation triggers based on robust detection of changes in underlying system dynamics or environmental conditions reflected in sensor data topology.52
• Improved Prediction: While some controllers incorporate preview information from dedicated sensors (e.g., cameras, lidar) 2, the proposed framework could achieve more sophisticated prediction. LLMs have demonstrated strong capabilities in sequence modeling and time series forecasting.11 When combined with the rich state representation derived from TDA, the LLM could potentially predict future road inputs, vehicle dynamic responses, or the likely consequences of control actions with greater accuracy and over longer horizons. This predictive capability can feed into the game-theoretic optimization, enabling proactive rather than purely reactive control.
• Increased Robustness: Real-world driving involves significant uncertainties, including unmodeled dynamics, sensor noise, varying environmental conditions, and unexpected events. Purely model-based controllers can suffer when the real system deviates from the model.1 Purely data-driven approaches might struggle with generalization to unseen scenarios or lack formal guarantees. The proposed hybrid approach combines data-driven elements (LLM learning, TDA feature extraction) with model-based optimization (Game Theory) and human oversight (HITL). This synergy can lead to greater robustness: TDA provides noise-resistant features 52, the LLM can reason about uncertainty and context 16, game theory can explicitly handle disturbances 39, and the human provides a fallback and validation layer.21

2.3 Potential for Adaptive, Predictive, and Robust Control

The true potential of this integrated framework lies in its ability to enable control actions that are simultaneously adaptive, predictive, and robust in ways that are difficult to achieve with conventional methods alone. Consider a scenario where the vehicle unexpectedly encounters a series of severe potholes not detected by forward-looking sensors.

1. Sensing & TDA: The suspension sensors register high-amplitude, sharp impacts. TDA applied to the vibration signals rapidly identifies a significant shift in the data’s topological signature, classifying it as an anomalous, high-severity event distinct from normal rough road patterns.52
2. LLM Comprehension & Goal Adaptation: The LLM receives the TDA output (e.g., features indicating high-impact anomaly) and integrates it with other context (e.g., current speed, vehicle load). It comprehends this as a situation demanding immediate prioritization of vehicle stability and component protection over ride comfort.15 Simultaneously, it processes any immediate human reaction captured via the HITL interface (e.g., sharp steering input, braking, verbal exclamation). Based on this comprehensive understanding, the LLM dynamically adjusts the objectives for the game theory module, drastically increasing the weighting for minimizing tire load variation and suspension deflection, while reducing the weight for minimizing body acceleration.14
3. Game Theoretic Optimization: The game theory solver receives these updated objectives and constraints. It computes a new optimal control strategy (actuator forces) that reflects the immediate priority shift, potentially stiffening the suspension response to maintain control and prevent bottoming out, even at the cost of momentary discomfort.3
4. Actuation & Feedback: The actuators execute the commands. The resulting vehicle motion is fed back through the sensors, allowing the TDA, LLM, and game theory modules to continuously monitor the situation and refine the response as the vehicle traverses the hazard. The HITL interface might inform the driver of the system’s action (“Stability prioritized due to severe road hazard”).

This example illustrates how the framework enables a rapid, context-aware adaptation driven by deep signal comprehension (TDA), intelligent reasoning (LLM), rigorous optimization (Game Theory), and human awareness (HITL), leading to a more robust and potentially safer response than a pre-programmed reaction.

Furthermore, the interaction between these components fosters the potential for the system to develop emergent intelligent behaviors. The framework is not simply executing pre-defined logic for different scenarios; it involves continuous information flow and adaptation across modules with different representational capabilities (semantic, topological, game-theoretic, human feedback).15 Through operation and potentially reinforcement learning mechanisms integrated within the game theory or LLM components 40, the system could learn complex, non-obvious correlations between subtle topological features in sensor data, semantic context, human preferences, and optimal game strategies. This could lead to the discovery and implementation of highly effective, nuanced control policies tailored to the specific vehicle, driver, and environment, exhibiting a form of intelligence that transcends the capabilities of any single component in isolation.

Section 3: Implementation Strategies (‘How’) – Core Components

This section details the methodologies and techniques required to implement the core components of the integrated active suspension control framework: the Edge LLM, the Human-Machine Interaction loop, the Game Theoretic formulation, and the Topological Signal Analysis module.

3.1 Edge LLM Adaptation for VCU Deployment

Deploying powerful LLMs on resource-constrained VCUs necessitates significant optimization across the model lifecycle, from pre-deployment model compression to runtime inference acceleration.

Optimization Techniques: A suite of techniques is employed to bridge the gap between the demands of LLMs and the limitations of edge hardware 8:

• Quantization: This is a cornerstone technique involving reducing the numerical precision of the LLM’s parameters (weights) and/or activations from standard 32-bit floating-point (FP32) to lower-bit formats like FP16, INT8, INT4, or even sub-4-bit representations.8

• Post-Training Quantization (PTQ): Applied to a pre-trained model without retraining. Common variants include:

• Weight-Only Quantization: Quantizes only the weight tensors, significantly reducing memory footprint, particularly beneficial for the memory-bound decoding stage.8 Examples include AWQ, which identifies and protects salient weights.8
• Weight-Activation Quantization: Quantizes both weights and activations, leveraging low-precision hardware capabilities (e.g., Tensor Cores) for accelerating compute-bound operations like matrix multiplication (GEMM) during the prefill stage.8 Techniques like SmoothQuant address challenges in quantizing activations.8
• KV Cache Quantization: Reduces the memory overhead associated with storing the attention mechanism’s key-value cache, crucial for handling long sequences or large batch sizes.13

• Quantization-Aware Training (QAT): Integrates the simulation of quantization effects into the model training or fine-tuning process, allowing the model to adapt its weights to minimize accuracy loss due to reduced precision.8 While potentially yielding better accuracy than PTQ, especially for very low bit-widths, QAT requires access to training data and significant computational resources.13
• Mixed-Precision: Strategically uses different precision levels for different parts of the model to balance efficiency and accuracy.9 Specialized formats like NVFP4 and support for INT4 (W4A16) and FP8 (W8A8) are emerging, often requiring specific hardware and software support (e.g., NVIDIA DRIVE AGX Thor, TensorRT versions).10
• Trade-offs: Quantization significantly reduces model size and memory usage, potentially speeds up inference, and lowers energy consumption. However, aggressive quantization (e.g., INT4 or lower) can lead to accuracy degradation if not carefully implemented.13

• Pruning: This technique involves removing redundant or unimportant parameters (weights) or structural components (neurons, attention heads, layers) from the LLM.8

• Unstructured Pruning: Removes individual weights, leading to sparse models that can achieve high compression rates but may require specialized hardware/software for efficient inference.8 Examples: SparseGPT.8
• Structured Pruning: Removes larger, regular blocks (e.g., entire neurons or channels), resulting in smaller dense models that are generally easier to accelerate on standard hardware.8 Examples: LLM-Pruner.8 Pruning reduces model size and computational cost (FLOPs).

• Knowledge Distillation (KD): A smaller “student” model is trained to mimic the behavior (outputs or internal representations) of a larger, pre-trained “teacher” model.8 This transfers the capabilities of the large model to a more compact and efficient one suitable for edge deployment. Various KD strategies exist, including white-box (accessing teacher internals) and black-box (using only teacher outputs) methods.8
• Architectural Choices: Selecting or designing LLM architectures that are inherently more compact and efficient is crucial. This includes using Small Language Models (SLMs) 11 or models specifically designed for edge deployment (e.g., LLaMA variants, Phi series, Gemma, OpenELM).8 Architectural innovations like grouped-query attention, multi-query attention, Rotary Position Embedding (RoPE), and optimized layer structures contribute to efficiency.8
• Runtime Optimizations: Techniques applied during inference execution:

• Speculative Decoding: Uses a smaller “draft” model to generate candidate token sequences, which are then verified by the main LLM, potentially accelerating text generation significantly.72
• Optimized Kernels & Attention Mechanisms: Using highly optimized implementations (e.g., CUDA-based kernels 10) for core operations like attention and matrix multiplication. Techniques like FlashAttention aim to reduce memory bandwidth bottlenecks.10
• Model Parallelism/Pipelining: Distributing model layers or computations across multiple processing units (if available on the edge device) or even across devices in collaborative edge scenarios.8
• Resource-Aware Scheduling & Execution: Dynamically managing computational resources and scheduling inference tasks efficiently on the edge device.8 Techniques like FlexGen manage memory and compute for large models.8
• Efficient Tokenization/Sampling: Using optimized tokenizers and sampling strategies (e.g., CUDA-based samplers with Top-K) to minimize overhead in the input and output stages.10

• Specialized SDKs and Frameworks: Leveraging dedicated software development kits (SDKs) and inference engines simplifies deployment and optimizes performance for specific hardware targets. Examples include:

• NVIDIA DriveOS LLM SDK: A lightweight toolkit built on TensorRT for NVIDIA DRIVE platforms, offering optimized kernels, quantization support (INT4, FP8, NVFP4), efficient tokenizers/samplers/decoders, and simplified deployment workflows (ONNX export, engine build).10
• Other Frameworks: TensorRT-LLM, llama.cpp, Ollama, MLX, CoreNet, TinyChatEngine provide optimized runtimes for various edge platforms (including CPUs, GPUs, NPUs).8

Table 1: Comparison of Edge LLM Optimization Techniques

 

Technique	Description	Typical Size Reduction	Typical Speedup	Accuracy Impact	Energy Efficiency	Key References / Examples
Quantization (PTQ)	Reduce precision post-training (e.g., FP32->INT8/INT4). Variants: Weight-only, Weight-Act, KV Cache.	2x – 8x+	1x – 4x+	Minimal to Moderate	Moderate to High	8 (AWQ, SmoothQuant, LLM.int8())
Quantization (QAT)	Simulate quantization during training/fine-tuning.	2x – 8x+	1x – 4x+	Minimal (potentially < PTQ)	Moderate to High	8
Pruning (Structured)	Remove larger blocks (neurons, channels, heads). Results in smaller dense models.	1.5x – 5x+	1.5x – 4x+	Moderate	Moderate	8 (LLM-Pruner, Sheared LLaMA)
Pruning (Unstructured)	Remove individual weights. Results in sparse models.	2x – 10x+	Hardware Dependant	Minimal to Moderate	Moderate	8 (SparseGPT, Wanda)
Knowledge Distillation	Train small ‘student’ model to mimic large ‘teacher’.	5x – 100x+	5x – 100x+	Varies (Goal: Minimal)	High	8 (DistilBERT, TinyBERT, MiniLM)
Architectural Choices	Use inherently smaller models (SLMs) or efficient architectures (attention variants, etc.).	N/A (Baseline)	N/A (Baseline)	Varies by model	High	8 (Phi, Gemma, LLaMA variants, OpenELM)
Speculative Decoding	Use fast ‘draft’ model to predict tokens, verify with main model.	None	2x – 3x+	Minimal	Low to Moderate	72
Optimized Kernels/SDKs	Use hardware-specific libraries & optimized implementations (e.g., attention, quantization support).	None	1.5x – 5x+	Minimal	Moderate	8 (TensorRT, DriveOS SDK, llama.cpp)

(Note: Values for Size Reduction, Speedup, etc., are indicative ranges and highly dependent on the specific model, task, hardware, and implementation details.)

Signal Processing Tasks for LLM: The adapted Edge LLM needs to perform specific tasks related to signal processing within the control loop:

• Comprehension: This involves interpreting the outputs generated by the TDA module. For instance, understanding that a specific pattern in the persistence diagram corresponds to driving on a cobblestone road, or that certain topological features indicate high vibration levels requiring intervention.15 It also includes understanding human input (textual or potentially verbal commands/feedback via HITL) 15 and fusing information from various sensors (e.g., correlating TDA features with camera-based road surface classification or IMU data).10 A key challenge here is bridging the gap between continuous numerical signals (or derived topological features) and the discrete token/text-based domain of LLMs.11 Techniques involve encoding numerical time series or features as strings of digits 19 or using statistically-enhanced prompting and adaptive fusion embeddings to align temporal patterns with the LLM’s token space.11
• Decision: Based on its comprehension of the current state (from TDA and other sensors) and context (including human input), the LLM makes high-level decisions or sets parameters for the downstream game-theoretic controller.16 This could involve deciding the overall control objective (e.g., prioritize comfort, handling, or energy saving), adjusting the weights in the game’s payoff function, identifying potential conflicts or hazards 16, or determining the appropriate control mode. The LLM’s reasoning capabilities are leveraged here.16
• Projection: LLMs, being powerful sequence models, can potentially be used for time series forecasting.11 In this context, the Edge LLM could predict future road inputs based on past TDA features and context, forecast the vehicle’s likely dynamic response to current conditions, or project the consequences of different potential control strategies. These projections can provide valuable lookahead information for the game theory module, enabling more proactive and anticipatory control.

Achieving efficient edge LLM deployment for the demanding real-time requirements of vehicle control necessitates a holistic approach. It’s not merely a software optimization problem; hardware-software co-design is imperative.8 The selection of specific optimization techniques, such as the choice between INT4, FP8, or other low-bit quantization formats 10, or the preference for structured versus unstructured pruning 8, should be directly informed by the capabilities of the underlying hardware accelerators (e.g., NPUs, specialized GPU cores, custom ASICs) present on the VCU.8 Frameworks like the NVIDIA DriveOS LLM SDK are explicitly designed for specific hardware platforms 10, highlighting this dependency. Optimizations must leverage the available hardware features to minimize latency and power consumption, ensuring the LLM can perform its comprehension, decision, and projection tasks within the strict time budgets of the suspension control loop.

3.2 Human-Machine Interaction Loop Design

Designing the interface and interaction mechanisms for the HITL component is critical for the system’s usability, effectiveness, and safety. It requires careful consideration of HCI principles and human factors.

Interface Principles: The design should adhere to established HCI guidelines 25:

• Transparency: The user should have a clear understanding of what the AI system is doing, why it’s doing it, and its current state or mode of operation. This builds trust and enables effective collaboration.23 Opaque AI decision-making hinders acceptance.24
• User Agency: The user should feel in control and be able to influence the system’s behavior meaningfully.25 The interface should provide appropriate levels of control granularity, avoiding overly simplistic “all-or-nothing” interactions.25
• Feedback: Clear, timely, and relevant feedback should be provided to the user about the system’s status, actions taken, and the impact of user inputs.30 This closes the interaction loop.30
• Minimize Cognitive Load: The interface should be intuitive and easy to use, minimizing mental effort required from the driver, especially considering the primary task of driving.21
• Consider Human Limitations: Design must account for human reaction times, potential for errors, varying levels of attention, and expertise.21
• Iterative Design: The interface should be developed through an iterative process involving prototyping, user testing, analysis, and refinement.30

Feedback Mechanisms: The system needs mechanisms for both receiving human input and providing system output:

• Input Modalities: How does the human provide input? Options include physical controls (buttons, dials), touchscreens, voice commands (leveraging the LLM’s natural language capabilities 15), gestures, or potentially even physiological sensors measuring driver state (e.g., stress, fatigue). Input types could range from explicit commands (“Set comfort mode”), preference settings (slider for comfort/handling balance), real-time corrections (“System response feels too harsh”), or validation signals (confirming a system suggestion).22
• Output Modalities: How is information conveyed to the human? Visual displays (dashboard, HUD), auditory cues (beeps, synthesized voice feedback), or haptic feedback (vibrations in the steering wheel or seat) can be used to communicate system status, confidence levels, intended actions, or requests for input.30

Real-time Validation and Adaptation: Integrating human validation into a real-time loop is challenging:

• Timeliness: Human validation or correction must occur within the time constraints of the control loop. Mechanisms for rapid intervention are needed, especially for safety-critical decisions.21
• Balancing Automation: Determining the right level of human involvement is key. Over-reliance on human input can slow the system down, while too little involvement can reduce trust and robustness.21 The system might dynamically adjust the need for human validation based on AI confidence or situation criticality.
• Continuous Improvement: Human feedback should ideally be used not just for immediate correction but also for long-term system improvement.22 This could involve Reinforcement Learning from Human Feedback (RLHF)-like mechanisms adapted for the control context, where human preferences guide the fine-tuning of the LLM or the game-theoretic payoffs.68 Continual learning techniques, like those proposed in Continual Human-in-the-Loop Optimization (CHiLO) using Bayesian methods and generative replay, could allow the system to personalize interactions and improve efficiency by leveraging experience accumulated across multiple users or sessions.75 Validation might involve comparing AI-suggested parameters or actions against human preferences or corrective inputs.29

Roles and Trust: Establishing a clear understanding of the human’s role (e.g., supervisor overseeing the AI, collaborator working alongside it, or director setting high-level goals) is essential for effective interaction.21 Building and maintaining trust is paramount.21 Trust is fostered through system reliability, predictability, transparency (explaining decisions), and giving the user appropriate agency.23 The system must also be robust to potential errors or even deliberate manipulation through human input.23

The design of the HITL interface inevitably confronts a trust-latency trade-off. Enhancing trust and user agency often involves providing more detailed explanations or requesting human validation at more frequent intervals.22 However, each interaction point introduces potential latency due to human perception, cognition, and action time.21 In a real-time control loop like active suspension, minimizing latency is critical for stability and performance.21 Therefore, the interface design must strategically select the points and methods for human interaction. Meaningful input should be solicited primarily where human judgment provides significant added value (e.g., setting subjective preferences, resolving high ambiguity) and where it can be provided within the strict temporal constraints of the control system. The level of required interaction might even adapt dynamically based on the perceived criticality or uncertainty of the current driving situation.

3.3 Game Theoretic Formulation for Suspension Control

Formulating the active suspension control problem using game theory requires defining the players, their available strategies, and the payoff functions that quantify their objectives.

Players: The definition of players depends on the specific aspect of the control problem being addressed:

• Controller vs. Disturbance: A common approach, especially in robust control formulations like H∞, treats the system as a zero-sum (or non-zero-sum) game between the controller (Player 1) and external disturbances (Player 2, representing road inputs or unmodeled dynamics).39 The controller aims to minimize a cost function representing deviations from desired performance (e.g., minimizing vibration), while the disturbance acts to maximize this cost. Solving this game leads to controllers robust against the worst-case disturbance.39
• Multi-Agent Coordination: When the system involves multiple interacting components, such as individual actuators at each wheel or coordination between suspension, steering, and braking (IVDC 4), these components can be modeled as distinct players.14 The game can be cooperative (players share a common goal) or non-cooperative (players optimize individual objectives, potentially conflicting). Seeking a Nash equilibrium in a non-cooperative game is a common approach to find a stable operating point where no single agent can improve its situation by changing its strategy alone.33 Potential games are particularly useful here, as convergence to a Nash equilibrium guarantees optimization of a global system objective.14

Strategies: Strategies represent the set of possible actions for each player.

• Controller: The strategies typically involve the control signals sent to the active suspension actuators, determining the force or torque they apply.2 In a dynamic game, this is a sequence of actions over time.
• Disturbance: The “strategies” of the disturbance player might represent the characteristics of the road profile or other external forces acting on the vehicle.
• Coordinating Agents: For multiple actuators or subsystems, strategies are their respective control inputs (e.g., force for each suspension actuator, steering angle, braking pressure).33

Payoff Functions (Utility/Cost): These functions mathematically encode the control objectives and are the most critical part of the game formulation, defining the desired behavior and trade-offs. They typically aggregate weighted terms representing different performance aspects:

• Ride Comfort: Penalties based on metrics like the RMS or peak values of sprung mass (body) vertical acceleration, pitch acceleration, and roll acceleration.1
• Handling/Stability: Penalties related to suspension deflection (to stay within working limits), dynamic tire load variation (to maintain road grip), and potentially body roll/pitch angles or yaw rate errors.3
• Energy/Actuator Effort: Penalties on the magnitude or rate of change of actuator forces or power consumption.3 This is crucial for multi-power systems and EVs.5
• Integration with LLM/HITL: The payoff function can be made adaptive. The LLM, based on its contextual understanding, or the human, via the HITL interface, can dynamically adjust the weighting factors associated with different objectives.14 For example, increasing the weight on comfort terms during smooth highway driving or increasing the weight on handling terms when the LLM detects sporty driving intentions or the user selects a “sport” mode.

Solution Concept: The goal is to find an optimal strategy profile according to the chosen game formulation:

• Nash Equilibrium (NE): For non-cooperative games, finding an NE provides a stable solution where each player optimizes their payoff given others’ strategies.33 Algorithms often involve iterative best response or learning dynamics.33
• Minimax/Maximin: For zero-sum games (Controller vs. Disturbance), the solution minimizes the maximum possible cost (worst-case scenario).40
• Potential Function Optimization: For potential games, algorithms aim to find the state that optimizes the global potential function, often corresponding to an NE.14
• Differential Games: For dynamic systems evolving over time, the solution often involves solving partial differential equations like the Hamilton-Jacobi-Bellman (HJB) equation for optimal control or the Hamilton-Jacobi-Isaacs (HJI) equation for zero-sum differential games.40 Reinforcement learning (RL) and approximate dynamic programming (ADP) techniques, often using actor-critic structures with neural networks, can be employed to find approximate solutions to these equations, especially when the system model is complex or partially unknown.40 Off-policy RL methods can learn from stored data without requiring direct interaction during learning.40

Table 2: Active Suspension Control Objectives and Game Theory Payoff Components

 

Primary Objective	Specific Metric Examples	Corresponding Payoff Term (Conceptual)	Potential Dynamic Weighting Factors (Source)	Key References
Ride Comfort	Body Vert. Accel. (RMS/Peak), Pitch Accel. (RMS), Roll Accel. (RMS)	Minimize: ∫wc​(t)⋅(z¨s2​+αθ¨2+βϕ¨​2)dt	wc​(t) (LLM Context, HITL Preference)	1
Handling/Stability	Suspension Deflection (RMS/Max), Tire Load Variation (RMS/Peak)	Minimize: ∫wh​(t)⋅((zs​−zu​)2+γFdyn2​)dt	wh​(t) (LLM Context, HITL Preference)	3
Energy Efficiency	Actuator Force (RMS/Peak), Actuator Power Consumption	Minimize: ∫we​(t)⋅(Fact2​ or Pact​)dt	we​(t) (LLM Context, Battery State, Mode)	3
Actuator Constraints	Max Actuator Force, Max Actuator Rate	Implicit via constraints or high penalty terms	Fixed or dynamically adjusted (LLM/HITL)	3

(Note: wx​(t) are time-varying weights, z¨s​,θ¨,ϕ¨​ are body vertical, pitch, roll accelerations, zs​−zu​ is suspension deflection, Fdyn​ is dynamic tire load, Fact​ is actuator force, Pact​ is actuator power. α,β,γ are relative weights. The integral represents cost over a time horizon. Actual formulations can vary significantly.)

The integration with an LLM and HITL introduces the possibility of an adaptive game structure. Beyond simply adjusting the weights (wx​(t)) within a fixed payoff function, the fundamental nature of the game might change based on higher-level inputs. For example, if the LLM, informed by TDA analysis of vibration patterns, infers a drastic change in road surface (e.g., transitioning from pavement to gravel), it might trigger a change in the disturbance model assumed within the game-theoretic formulation. Similarly, if the human driver selects an “off-road” mode via the HITL interface, the entire payoff structure might be reconfigured to prioritize robustness and large suspension travel over fine-grained comfort optimization. This dynamic reconfiguration of the game itself, driven by semantic understanding or explicit human commands, represents a deeper level of adaptation than traditional gain scheduling or parameter tuning within a fixed control structure.

3.4 Topological Signal Analysis Application

Applying TDA, Homotopy concepts, and Manifold Learning to suspension sensor data involves transforming the raw signals into topological or geometric representations and extracting meaningful features.

Data Source: The primary inputs are time-series data streams from sensors integral to the suspension and vehicle dynamics measurement:

• Accelerometers: Measuring vertical, lateral, and longitudinal accelerations of the sprung mass (vehicle body) and unsprung masses (wheels).62
• Suspension Displacement Sensors: Measuring the relative travel between the body and the wheels.1
• Gyroscopes/IMUs: Measuring pitch, roll, and yaw rates of the vehicle body.
• Other potential sources: Strain gauges on suspension components, wheel speed sensors, GPS data. Vibration signals are particularly rich sources of information about road conditions and system health.52

Representation: Raw time-series data needs to be transformed into a format amenable to topological analysis:

• Point Cloud Embedding: A standard technique is time-delay embedding (TDE), inspired by Takens’ theorem.50 A scalar time series x(t) is transformed into points in a higher-dimensional space Y(t)=[x(t),x(t−τ),x(t−2τ),…,x(t−(m−1)τ)], where m is the embedding dimension and τ is the time delay. This reconstructed state space can reveal the underlying attractor dynamics of the system.54 Multiple sensor signals can be combined into higher-dimensional point clouds.
• Graph/Complex Construction: Alternatively, topological structures can be built directly. For instance, a Vietoris-Rips complex can be constructed on the point cloud by connecting points within a certain distance threshold r; higher-dimensional simplices (triangles, tetrahedra) are added if all their vertices are pairwise connected.52 Other complexes like Čech or Alpha complexes can also be used. Graph representations can capture relationships between different sensors or time points.48

Analysis Techniques: Once data is represented topologically, various methods can extract structural information:

• Persistent Homology (PH): Applied to the point cloud (via Rips or other complexes) or directly to functions defined on the data (e.g., sublevel set filtration of signal intensity 53). PH computes persistence diagrams or barcodes that quantify the birth and death scales of topological features (connected components, loops, voids).45 The persistence of these features indicates their significance. PH is robust to noise and can capture multi-scale structure, making it suitable for analyzing complex vibration signals, detecting regime changes (e.g., road surface changes), or identifying fault signatures.52
• Manifold Learning: Algorithms like Isomap, LLE, t-SNE, Diffusion Maps, etc., are applied to the high-dimensional sensor data (or TDE point clouds) under the assumption that the system’s dynamics evolve on a lower-dimensional manifold.55 These methods aim to find a low-dimensional embedding that preserves essential geometric properties of the data, potentially revealing the intrinsic coordinates or state variables governing the suspension dynamics.58 This can be used for visualization, dimensionality reduction, or identifying distinct operational states.55
• Homotopy Methods (Conceptual): While direct computation of homotopy groups can be complex, the underlying concepts inform TDA. Homotopy equivalence can be used to compare the ‘shape’ of data from different time windows or conditions.45 A-homotopy might be relevant for analyzing discrete state transitions or the structure of graphs derived from sensor correlations.48 The Mapper algorithm 45 uses related ideas to create simplified network representations.

Interpretation and Use: The outputs of TDA (e.g., persistence diagrams, Betti numbers βi​, manifold coordinates, Mapper graphs) are typically abstract features that require interpretation in the context of the physical system. These features can then be utilized by other components of the framework:

• Input to LLM: Topological features provide a compact, robust summary of the complex sensor data. The LLM can be trained or prompted to comprehend the meaning of these features (e.g., “High β1​ persistence at medium scales indicates resonant vibrations on a corrugated surface”).15
• State Variables for Game Theory: TDA features can augment or replace traditional state variables in the game formulation. For example, the number or persistence of loops (β1​) in a vibration signal’s embedding might directly inform the ‘roughness’ parameter affecting the comfort cost in the payoff function. Manifold coordinates could serve as the low-dimensional state representation for the game dynamics.
• Anomaly/Fault Detection: Deviations from the expected or ‘normal’ topological signature (e.g., appearance of new persistent features, significant changes in Betti numbers or manifold structure) can serve as powerful indicators of system faults, component degradation, or unexpected operating conditions.52

Essentially, TDA and related methods function as a form of advanced, automated feature engineering specifically tailored for complex dynamic signals. Rather than relying on pre-defined statistical measures (like RMS 1) or frequency analysis (like FFT 62), TDA systematically probes the intrinsic geometric and topological structure of the sensor data itself.45 It extracts features that reflect the shape, connectivity, and multi-scale organization of the system’s trajectory in state space. These topologically derived features are often inherently robust to noise and certain transformations 52 and can capture non-linear relationships or subtle structural changes that might be missed by conventional techniques. By automatically discovering potentially more informative features directly from the data’s ‘shape’, TDA provides richer inputs for sophisticated downstream tasks like the semantic interpretation performed by the LLM or the state-dependent optimization carried out by the game theory module.

Section 4: Implementation Strategies (‘How’) – System Integration

Integrating the distinct components – Edge LLM, HITL interface, Game Theory module, and TDA module – into a cohesive and functional real-time control system requires careful planning of the overall architecture, data flow, and interfaces.

4.1 Process Outline for Integration

A systematic approach is necessary to manage the complexity of integrating these diverse technologies:

1. Component Development and Unit Testing: Each module (optimized Edge LLM, HITL interface software/hardware, Game Theory solver algorithms, TDA processing pipeline) must first be developed and rigorously tested individually. This involves simulation-based validation and potentially hardware-in-the-loop (HIL) testing to verify functionality, performance (latency, resource usage), and robustness against expected inputs and edge cases.
2. Interface Specification: Clear and precise definitions of the interfaces between modules are crucial. This includes specifying data formats (e.g., how topological features from TDA are represented for the LLM, how LLM outputs parameterize the game theory module, how human feedback is encoded), communication protocols (e.g., inter-process communication on the VCU, network protocols if distributed), data exchange rates, and timing constraints. Standardized interfaces facilitate modularity and independent development.
3. Data Flow Architecture Design: A detailed architecture mapping the flow of information through the system must be designed. This involves tracing the path from sensor acquisition, through preprocessing and TDA, into the state representation layer, then to the LLM (which also receives HITL input), onwards to the Game Theory module, and finally to the low-level controllers and actuators. Crucially, all necessary feedback loops must be defined: sensor data feeding back the results of actions, system state information being presented to the human via the HITL interface, and human feedback influencing the LLM and/or Game Theory module.
4. Integrated Simulation Environment: A comprehensive simulation environment is essential for testing the interactions between components before deployment on the actual vehicle hardware. This environment should include accurate models of the vehicle dynamics (e.g., full-vehicle models 1), suspension components, actuator dynamics 42, sensor characteristics (including noise models), realistic road profiles 1, and software representations of the TDA, LLM, HITL, and Game Theory modules. Tools like MATLAB/Simulink 60, CarSim, or other vehicle dynamics simulation platforms can be utilized.
5. Real-Time Implementation and Hardware Testing: The integrated software stack must be deployed onto the target VCU hardware. This stage involves addressing platform-specific challenges, optimizing for real-time execution, and managing resource allocation (CPU, memory, accelerators). Rigorous testing in real-time is required, often starting with HIL setups before moving to prototype vehicles. Coordination strategies, potentially drawing from Integrated Vehicle Dynamics Control (IVDC) principles that emphasize avoiding conflicts between chassis subsystems 4, should guide the integration logic.
6. System Validation and Refinement: The final stage involves extensive validation of the complete system. This includes systematic testing across a wide range of driving scenarios, road conditions, and environmental factors. Performance should be benchmarked against traditional controllers and safety requirements must be rigorously verified. Feedback from test drivers interacting with the HITL system is invaluable for assessing usability, performance, and trust.22 The results from validation feed back into an iterative refinement process, tuning parameters, improving algorithms, and potentially redesigning interfaces or components.

4.2 Data Flow Architecture and Component Interactions

A plausible data flow architecture for the integrated system can be conceptualized as follows:

• Sensors & Preprocessing: Data streams from various sensors (suspension accelerometers, displacement sensors, vehicle IMU, GPS, potentially cameras/lidar for context, driver input sensors for HITL) are acquired, time-stamped, and synchronized. Initial preprocessing steps like filtering, noise reduction, and unit conversion are applied.
• TDA Module: Selected preprocessed sensor streams (e.g., high-frequency vibration data, suspension travel) are fed into the TDA module. This module performs operations like time-delay embedding, point cloud construction, persistent homology computation, or manifold learning. Its output consists of topological/geometric features (e.g., persistence diagrams summaries like Betti numbers, key feature lifetimes, manifold coordinates) or symbolic state classifications (e.g., ‘smooth road’, ‘rough road’, ‘pothole detected’).
• State Representation Layer: This layer aggregates information from multiple sources into a unified state vector or context description for the LLM. Inputs include: processed raw sensor data (speed, yaw rate etc.), TDA module outputs, potentially perception system outputs (object detection, lane information), map data, and current vehicle status (e.g., battery level, selected driving mode).
• Edge LLM Core: The LLM receives the comprehensive state representation and any concurrent input from the HITL interface (e.g., driver preference adjustments, commands). It performs its core functions:

• Comprehension: Interprets the state representation, understanding the significance of TDA features, sensor readings, and contextual factors.
• Reasoning: Assesses the situation, considers potential risks or opportunities, and aligns with human input.
• Decision/Goal Setting: Determines the high-level control objectives or strategy for the current context. This translates into specific parameters for the Game Theory module, such as updated weights for comfort vs. handling in the payoff function, modified constraints, or selection of a specific game formulation.

• HITL Interface: This module serves as the bidirectional communication channel with the human driver. It presents relevant information synthesized by the LLM (e.g., current control mode, system confidence, rationale for actions, potential hazards detected). It captures driver inputs (e.g., mode selection, comfort/handling slider adjustments, validation responses, verbal commands) and relays them primarily to the LLM for interpretation and integration into the decision-making process.
• Game Theory Module: Receives the dynamically adjusted goals, parameters, and constraints from the LLM. It also takes the current, lower-level vehicle state (e.g., positions, velocities) as input. Based on the selected game formulation (e.g., Controller vs. Disturbance, multi-agent coordination), it solves the optimization problem in real-time (e.g., finds the Nash equilibrium, solves the HJI equation approximately). The output is the optimal control action, typically represented as desired forces or torques for each active suspension actuator.
• Low-Level Control & Actuation: This layer translates the desired forces/torques from the Game Theory module into specific commands for the physical actuators (e.g., servo valve positions for hydraulic actuators, current commands for electromagnetic actuators). It incorporates actuator models, handles saturation limits, and implements low-level feedback loops to ensure accurate force tracking.
• Feedback Loops: Multiple feedback loops operate concurrently: the primary control loop (sensors -> TDA -> LLM -> Game -> Actuators -> Vehicle Dynamics -> Sensors), the human interaction loop (System State -> HITL Output -> Human -> HITL Input -> LLM), and internal loops within components (e.g., actuator force control).

A significant hurdle in realizing this integrated architecture lies in managing the communication bottlenecks and bridging the semantic gap between modules operating in fundamentally different domains. The LLM operates primarily in a symbolic, linguistic space, processing tokens and semantic concepts.15 In contrast, the TDA module produces abstract geometric or topological features (persistence diagrams, Betti numbers, manifold embeddings) 49, and the Game Theory module operates on numerical states, strategies, and payoff values defined within a mathematical framework.3 Efficiently and accurately translating information across these boundaries is critical. For instance, how can the rich, multi-scale information contained in a persistence diagram 52 be summarized into a format that the LLM can meaningfully comprehend without losing crucial detail? Conversely, how does a high-level, potentially ambiguous human command like “make the ride less floaty” 15 get translated by the LLM into precise, quantitative adjustments to the weighting factors or constraints within the game’s payoff function?34 Designing these intermediate representations and translation mechanisms is a non-trivial challenge. Ineffective translation could lead to information loss (the semantic gap), misunderstandings between modules, or communication delays (the bottleneck), ultimately compromising the performance and reliability of the entire system. Robust and efficient interface layers are therefore essential for successful integration.

Section 5: Challenges, Limitations, and Future Directions

While the proposed integrated framework offers significant potential, its development and deployment face substantial challenges spanning computational performance, safety validation, system complexity, data requirements, and the fundamental difficulty of merging disparate theoretical paradigms.

5.1 Computational, Real-Time, and Resource Constraints

Meeting the stringent real-time requirements of active suspension control (often demanding cycle times in the low milliseconds) with computationally intensive components poses a major hurdle:

• Edge LLM Inference Latency: Executing inference for large language models, even after optimization, on resource-constrained VCUs is computationally demanding.7 Tasks involving complex reasoning, processing long contexts, or generating detailed projections can incur significant latency, potentially exceeding the control loop’s time budget.16 The effectiveness of optimization techniques like quantization and pruning must be carefully balanced against potential accuracy degradation.13
• TDA Computation Cost: Algorithms for TDA, particularly persistent homology computation for large point clouds or high-dimensional embeddings using methods like Vietoris-Rips complex construction, can be computationally intensive.45 Similarly, manifold learning algorithms often involve nearest neighbor searches and eigenvalue decompositions, which can be costly for large datasets.56 Performing these analyses within the required real-time constraints on VCU hardware is challenging.
• Game Theory Solution Time: Solving the optimization problems inherent in game theory, especially dynamic or differential games requiring the solution of HJI equations or iterative Nash equilibrium seeking, can be computationally expensive.34 Approximate methods using RL or ADP might reduce the burden but introduce their own complexities and convergence time issues.40
• Cumulative System Latency: The overall latency is the sum of delays across each stage of the processing pipeline (Sensor -> Preprocessing -> TDA -> State Representation -> LLM -> Game Theory -> Low-Level Control -> Actuator). Managing and minimizing this cumulative latency is critical for system stability and responsiveness. Delays in any component can degrade the performance of the entire control loop.

5.2 Safety, Validation, Reliability, and Trustworthiness

Ensuring the safety, reliability, and trustworthiness of such a complex, adaptive system is perhaps the most significant challenge:

• Safety Validation: Traditional validation methods for automotive software often rely on exhaustive testing and formal verification. These approaches are difficult, if not impossible, to apply comprehensively to a system incorporating non-deterministic components like LLMs (prone to potential “hallucinations” or unexpected outputs 16) and unpredictable elements like human interaction.21 Guaranteeing safe behavior across all possible scenarios and interactions is a major open problem.77 New validation methodologies focusing on runtime monitoring, extensive simulation, statistical guarantees, and robust fail-safe mechanisms are likely required.
• Reliability: The system must be robust against a wide range of potential failures, including sensor malfunctions, actuator faults, software errors in any component, communication dropouts, and unexpected environmental conditions (edge cases). Furthermore, the reliability of the human operator within the loop (influenced by factors like fatigue, distraction, or expertise) must be considered.21 The system also needs resilience against potentially incorrect or even adversarial inputs from the human.23
• Trustworthiness and Explainability: Building trust – both from the end-user (driver) and regulatory bodies – is essential for acceptance.21 The inherent complexity and the presence of “black-box” components like deep neural networks within the LLM, TDA feature extractors (if ML-based), or RL-based game solvers make the system difficult to understand and interpret. A lack of transparency hinders trust.23 Developing effective Explainable AI (XAI) techniques that can articulate the reasoning behind the system’s decisions (e.g., why the LLM chose a specific objective weighting, why the TDA module flagged an anomaly, why the game solver selected a particular action) is crucial but challenging.25
• Ethical Considerations: Potential biases embedded in the LLM’s training data or introduced through human feedback could lead to unfair or undesirable system behavior. Establishing clear lines of accountability in the event of system failure involving multiple interacting AI and human components is also a complex ethical and legal issue.22

5.3 Complexity of Integrating Diverse Theoretical Frameworks

The framework’s strength – its integration of diverse technologies – is also a source of significant complexity:

• Interdisciplinary Expertise: Successful development requires a team with deep expertise spanning multiple, often disparate fields: AI and machine learning (LLMs, RL), control theory (active suspension dynamics, game theory, robust control), human factors and HCI (HITL design), and advanced mathematics (topology, differential geometry for TDA/manifolds). Bridging the conceptual and technical vocabularies and methodologies across these domains is challenging.
• Interface Design Complexity: As highlighted previously (Insight 4.1), designing the interfaces and translation layers between components operating on fundamentally different principles (symbolic LLM vs. numerical game theory vs. topological TDA) is a complex task requiring careful consideration of information representation, potential loss of fidelity, and communication efficiency.
• System-Level Tuning and Optimization: The overall system involves numerous interacting parameters: LLM hyperparameters and prompt engineering, HITL interface settings and feedback gains, TDA algorithm parameters (e.g., embedding dimensions, persistence thresholds), and game theory payoff function weights and solver parameters. Tuning this high-dimensional parameter space to achieve optimal, stable, and robust performance across all operating conditions is a formidable optimization problem itself.

5.4 Data Requirements and Sensor Fusion

The data-driven components of the system impose significant data requirements:

• Data Volume and Quality: Training or fine-tuning the Edge LLM 11, developing robust TDA models capable of distinguishing various states and anomalies, and potentially training RL agents for the game theory module necessitates large volumes of high-quality, diverse, and accurately labeled data. This data must cover a wide range of driving scenarios, road types, weather conditions, vehicle states, and potential fault conditions. Acquiring and curating such datasets is expensive and time-consuming.
• Sensor Suite: The framework relies on a comprehensive and reliable suite of sensors providing accurate, high-frequency measurements of suspension state, vehicle dynamics, potentially environmental perception (cameras, lidar), and human inputs. Sensor noise, bias, and potential failures must be handled robustly.
• Sensor Fusion: Effectively integrating and fusing data from these heterogeneous sensors in real-time to create a consistent and accurate state representation for the TDA and LLM modules is a non-trivial task, requiring sophisticated algorithms and careful time synchronization.

The introduction of components designed to handle complexity and uncertainty (LLM, HITL, TDA) paradoxically complicates the validation process. Traditional validation relies heavily on predictability and the ability to test against well-defined specifications. However, the adaptive, potentially non-deterministic nature of the LLM 16, the inherent variability of human behavior in the HITL system 21, and the complex, emergent features identified by TDA make exhaustive testing impossible and formal verification exceedingly difficult. The system’s ability to adapt and handle novelty is precisely what makes it powerful, but also what makes it challenging to validate using conventional methods. This creates a validation paradox: the tools employed to manage complexity themselves introduce validation complexities, demanding new approaches likely centered on rigorous simulation, statistical evaluation over large datasets, runtime monitoring with safety guards, and clearly defined operational design domains.

5.5 Future Research Avenues

Addressing the aforementioned challenges requires further research and development across several areas:

• Efficient and Real-Time TDA/Homotopy: Development of faster, computationally lighter algorithms for persistent homology, manifold learning, and other topological techniques suitable for real-time execution on embedded automotive hardware. This might involve approximate methods, hardware acceleration, or novel topological feature extraction approaches.
• Explainable AI (XAI) for Integrated Systems: Creating methods to provide meaningful explanations for the decisions made by the integrated system, particularly the LLM and game theory components, to enhance transparency, debuggability, and user trust. Explanations need to be suitable for both human operators (in real-time) and system validators (for offline analysis).
• Formal Verification and Runtime Assurance: Investigating techniques for formally verifying safety and stability properties of hybrid systems involving AI components and human interaction, potentially focusing on bounded guarantees or safety envelopes. Developing robust runtime monitoring and assurance techniques to detect and mitigate unsafe conditions during operation.
• Online Learning and Adaptation: Researching methods that allow the system components (LLM, game strategies, TDA models) to safely learn and adapt online from operational data and human feedback, continuously improving performance while maintaining stability and safety guarantees. Continual learning approaches 75 are relevant here.
• Robustness Against Adversarial Inputs: Developing techniques to make the system resilient to noisy sensor data, unexpected environmental inputs, and potentially malicious or misleading feedback from the human user or external sources (e.g., V2X communication).
• Benchmarking and Standardization: Establishing standardized benchmarks, datasets, and evaluation metrics specifically designed for assessing the performance, safety, and efficiency of complex, integrated control systems like the one proposed. This is crucial for comparing different approaches and driving progress in the field.

Table 3: Summary of Key Development Challenges and Potential Mitigation Strategies

 

Challenge Category	Specific Issue Example	Potential Mitigation Strategies	Key References
Real-Time Performance	Edge LLM Inference Latency	Advanced Optimization (Quantization, Pruning, SDKs 10), Hardware Acceleration (NPUs, GPUs 9), Efficient Architectures (SLMs 11)	7
	TDA Computation Cost	Faster Algorithms (Approximations), Hardware Acceleration, Optimized Libraries (e.g., for Ripser 50), Simpler Topological Features	45
	Game Theory Solution Time	Approximate Solvers (RL/ADP 40), Efficient Algorithms for Specific Game Classes (Potential Games 34), Parallelization	34
Safety & Validation	Guaranteeing Safe Behavior (LLM Hallucinations, HITL Errors)	Runtime Monitoring, Formal Methods (Bounded Verification), Fail-Safe Mechanisms, Extensive Simulation, Rigorous HITL Protocols, Redundancy	16
	Lack of Comprehensive Validation Methods	Statistical Validation, Scenario-Based Testing, Operational Design Domains (ODDs), Focus on Runtime Assurance, New Validation Frameworks	22
Integration Complexity	Semantic Gap (LLM vs. Numerical/Topological)	Robust Interface Design, Intermediate Representations, Multi-modal LLMs, Careful Feature Engineering/Selection	11
	System-Level Tuning	Automated Tuning Methods (e.g., Bayesian Optimization 75), Modular Design, Simulation-Based Optimization	75
Data Requirements	Need for Large, Diverse, Labeled Datasets	Data Augmentation, Synthetic Data Generation, Transfer Learning, Semi-Supervised Learning, Active Learning (guided by HITL)
Trust & Explainability	Black-Box Nature of AI Components	Explainable AI (XAI) Techniques, Transparency Mechanisms in HITL Interface 25, Confidence Scoring 29, Clear Communication of System State/Intent	22

Conclusion

This report has delineated a sophisticated and highly integrated framework for the control of multi-power, fully active vehicle suspension systems. By synergistically combining Edge Intelligence LLMs, Human-in-the-Loop interaction, Game Theory optimization, and Topological Data Analysis, the proposed system architecture aims to achieve a paradigm shift in vehicle dynamics control, moving beyond the limitations of conventional methods. The potential benefits include enhanced adaptability to complex and uncertain driving conditions, optimized balancing of conflicting performance objectives (ride comfort, handling stability, energy efficiency), improved robustness through multi-layered intelligence and oversight, and greater personalization via human interaction.

The core rationale lies in leveraging each technology’s unique strengths: the LLM’s capacity for semantic comprehension and contextual reasoning; HITL’s incorporation of human judgment, preference, and validation; Game Theory’s rigorous framework for optimal decision-making under conflict; and TDA’s ability to extract robust, meaningful features from complex sensor signals. The integration promises a system capable of understanding the driving situation with unprecedented depth, making informed, optimized decisions, and adapting dynamically to both environmental changes and user intent.

However, the realization of such a framework presents formidable challenges. Optimizing and deploying complex LLMs and TDA algorithms on resource-constrained VCUs while meeting stringent real-time requirements remains a significant hurdle. Ensuring the safety, reliability, and trustworthiness of a system involving interacting AI components and human variability necessitates novel validation and verification paradigms that go beyond traditional approaches. The inherent complexity of integrating diverse theoretical frameworks and managing the intricate data flows and interfaces requires substantial interdisciplinary expertise and careful system engineering. Furthermore, significant efforts in data acquisition, sensor fusion, and robust HITL interface design are essential.

Despite these challenges, the potential impact of successfully developing such an integrated system is profound. It could lead to vehicles offering vastly superior ride quality and handling characteristics, adapting seamlessly to any road condition or driving style while optimizing energy usage. The principles explored – combining semantic AI, formal optimization, advanced signal processing, and human collaboration – may also have broader implications for the design of future intelligent autonomous systems in various domains beyond automotive control. While significant research and development are required to overcome the identified obstacles, the integrated framework presented here offers a compelling vision for the future of intelligent vehicle dynamics control.

References

(Note: In a final report, this section would contain a formatted list of all sources referenced using the snippet IDs, potentially expanded with full bibliographic details if available.)

1

Works cited

1. Evaluation of ride performance of PID controller in active suspension …, accessed April 23, 2025, https://www.extrica.com/article/24545
2. The Future Development and Analysis of Vehicle … – ResearchGate, accessed April 23, 2025, https://www.researchgate.net/profile/Nouby-Ghazaly/publication/266384689_The_Future_Development_and_Analysis_of_Vehicle_Active_Suspension_System/links/542eb0ab0cf277d58e8ed42a/The-Future-Development-and-Analysis-of-Vehicle-Active-Suspension-System.pdf
3. Optimization of the Semi-Active-Suspension Control of BP Neural Network PID Based on the Sparrow Search Algorithm – MDPI, accessed April 23, 2025, https://www.mdpi.com/1424-8220/24/6/1757
4. Integrated Vehicle Dynamics Control —state-of-the art review – ResearchGate, accessed April 23, 2025, https://www.researchgate.net/publication/224350373_Integrated_Vehicle_Dynamics_Control_-state-of-the_art_review
5. Active Suspension Explained – Domin, accessed April 23, 2025, https://domin.com/blog/what-is-active-suspension/
6. Active Suspension Bolstered by High Density Power Modules …, accessed April 23, 2025, https://www.vicorpower.com/resource-library/articles/automotive/active-suspension-bolstered-by-high-density-power-modules
7. Towards Edge General Intelligence via Large Language Models: Opportunities and Challenges – arXiv, accessed April 23, 2025, https://arxiv.org/html/2410.18125v3
8. (PDF) A Review on Edge Large Language Models: Design …, accessed April 23, 2025, https://www.researchgate.net/publication/384974008_A_Review_on_Edge_Large_Language_Models_Design_Execution_and_Applications
9. Advances to low-bit quantization enable LLMs on edge devices – Microsoft Research, accessed April 23, 2025, https://www.microsoft.com/en-us/research/blog/advances-to-low-bit-quantization-enable-llms-on-edge-devices/
10. Streamline LLM Deployment for Autonomous Vehicle Applications …, accessed April 23, 2025, https://developer.nvidia.com/blog/streamline-llm-deployment-for-autonomous-vehicle-applications-with-nvidia-driveos-llm-sdk/
11. Small but Mighty: Enhancing Time Series Forecasting with Lightweight LLMs – arXiv, accessed April 23, 2025, https://arxiv.org/html/2503.03594v1
12. MoE$^2$: Optimizing Collaborative Inference for Edge Large Language Models – arXiv, accessed April 23, 2025, https://arxiv.org/abs/2501.09410
13. Sustainable LLM Inference for Edge AI: Evaluating Quantized LLMs for Energy Efficiency, Output Accuracy, and Inference Latency – arXiv, accessed April 23, 2025, https://arxiv.org/html/2504.03360v1
14. On game-based control systems and beyond, accessed April 23, 2025, https://academic.oup.com/nsr/article-pdf/7/7/1120/38881794/nwaa019.pdf
15. Semantic Digital Twins: Enhancing Performance in Wireless Communication and LLM Inference – Huawei, accessed April 23, 2025, https://www.huawei.com/en/huaweitech/future-technologies/semantic-digital-twins-wireless-communication-llm-inference
16. The Crossroads of LLM and Traffic Control: A Study on Large Language Models in Adaptive Traffic Signal Control | Request PDF – ResearchGate, accessed April 23, 2025, https://www.researchgate.net/publication/386164563_The_Crossroads_of_LLM_and_Traffic_Control_A_Study_on_Large_Language_Models_in_Adaptive_Traffic_Signal_Control
17. CVPR Poster Beyond Text: Frozen Large Language Models in Visual Signal Comprehension, accessed April 23, 2025, https://cvpr.thecvf.com/virtual/2024/poster/30250
18. Position Paper: What Can Large Language Models Tell Us about Time Series Analysis, accessed April 23, 2025, https://arxiv.org/html/2402.02713v1
19. Large Language Models Are Zero-Shot Time Series Forecasters – arXiv, accessed April 23, 2025, https://arxiv.org/pdf/2310.07820
20. CoLLMLight: Cooperative Large Language Model Agents for Network-Wide Traffic Signal Control – arXiv, accessed April 23, 2025, https://arxiv.org/html/2503.11739v1
21. (PDF) A Survey on Human in the Loop for Self-Adaptive Systems, accessed April 23, 2025, https://www.researchgate.net/publication/386213033_A_Survey_on_Human_in_the_Loop_for_Self-Adaptive_Systems
22. Human in the Loop AI: Keeping AI Aligned with Human Values – Holistic AI, accessed April 23, 2025, https://www.holisticai.com/blog/human-in-the-loop-ai
23. Human-in-the-loop or AI-in-the-loop? Automate or Collaborate? – arXiv, accessed April 23, 2025, https://arxiv.org/html/2412.14232v1
24. What is Human AI Collaboration? – Aisera, accessed April 23, 2025, https://aisera.com/blog/human-ai-collaboration/
25. Humans in the Loop: The Design of Interactive AI Systems | Stanford HAI, accessed April 23, 2025, https://hai.stanford.edu/news/humans-loop-design-interactive-ai-systems
26. A Framework for Reasoning About the Human in the Loop – USENIX, accessed April 23, 2025, https://www.usenix.org/legacy/event/upsec08/tech/full_papers/cranor/cranor.pdf
27. Survey on Human-Vehicle Interactions and AI Collaboration for Optimal Decision-Making in Automated Driving – arXiv, accessed April 23, 2025, https://arxiv.org/html/2412.08005v1
28. [2412.08005] Survey on Human-Vehicle Interactions and AI Collaboration for Optimal Decision-Making in Automated Driving – arXiv, accessed April 23, 2025, https://arxiv.org/abs/2412.08005
29. Human-AI Collaboration: Keep The Machines On A Short Leash – Forbes, accessed April 23, 2025, https://www.forbes.com/sites/joemckendrick/2024/11/28/human-ai-collaboration-keep-the-machines-on-a-short-leash/
30. Human–computer interaction – Wikipedia, accessed April 23, 2025, https://en.wikipedia.org/wiki/Human%E2%80%93computer_interaction
31. Human models in human-in-the-loop control systems – ResearchGate, accessed April 23, 2025, https://www.researchgate.net/publication/337880739_Human_models_in_human-in-the-loop_control_systems
32. Human-In-The-Loop Control and Task Learning for Pneumatically Actuated Muscle Based Robots – Frontiers, accessed April 23, 2025, https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2018.00071/full
33. Game Theory-Based Interactive Control for Human–Machine … – MDPI, accessed April 23, 2025, https://www.mdpi.com/2076-3417/14/6/2441
34. Optimization via game theoretic control – PMC, accessed April 23, 2025, https://pmc.ncbi.nlm.nih.gov/articles/PMC8289022/
35. Self-driving skillset – game theory for autonomous vehicles – TechHQ, accessed April 23, 2025, https://techhq.com/2023/03/self-driving-skillset-game-theory-for-autonomous-vehicles/
36. Applications of Game Theory in Vehicular Networks: A Survey – ResearchGate, accessed April 23, 2025, https://www.researchgate.net/publication/354232036_Applications_of_Game_Theory_in_Vehicular_Networks_A_Survey
37. On game-based control systems and beyond, accessed April 23, 2025, https://academic.oup.com/nsr/article-pdf/7/7/1122/38882117/nwaa046.pdf
38. Game theory approaches for autonomy – Frontiers, accessed April 23, 2025, https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2022.880706/full
39. Linear Quadratic Optimal and Risk-Sensitive Control for Vehicle Active Suspensions, accessed April 23, 2025, https://www.researchgate.net/publication/260522038_Linear_Quadratic_Optimal_and_Risk-Sensitive_Control_for_Vehicle_Active_Suspensions
40. H∞ Differential Game of Nonlinear Half-Car Active Suspension via Off-Policy Reinforcement Learning – MDPI, accessed April 23, 2025, https://www.mdpi.com/2227-7390/12/17/2665
41. (PDF) H∞ Differential Game of Nonlinear Half-Car Active Suspension via Off-Policy Reinforcement Learning – ResearchGate, accessed April 23, 2025, https://www.researchgate.net/publication/383459917_H_Differential_Game_of_Nonlinear_Half-Car_Active_Suspension_via_Off-Policy_Reinforcement_Learning
42. Road adaptive active suspension design using linear parameter-varying gain-scheduling, accessed April 23, 2025, https://www.researchgate.net/publication/3332324_Road_adaptive_active_suspension_design_using_linear_parameter-varying_gain-scheduling
43. Vol. 11, No. 1, 2024 – IEEE/CAA Journal of Automatica Sinica, accessed April 23, 2025, https://www.ieee-jas.net/article/2024/1
44. Analysis of the Influence of Suspension Actuator Limitations on Ride Comfort in Passenger Cars Using Model Predictive Control – MDPI, accessed April 23, 2025, https://www.mdpi.com/2076-0825/9/3/77
45. Homotopy Theory and Topological Data Analysis | SciTechnol, accessed April 23, 2025, https://www.scitechnol.com/peer-review/homotopy-theory-and-topological-data-analysis-0NFV.php?article_id=26944
46. Homotopy Theory: An Introduction to Algebraic Topolopy, accessed April 23, 2025, https://www.maths.ed.ac.uk/~v1ranick/papers/gray.pdf
47. View of Redundant Mathematical Solution for Complex Homotopy Structures using Graph Theory based on Bipartite Chromatic Polynomial for Solving Distance Problems – Journal of Information Systems Engineering and Management, accessed April 23, 2025, https://jisem-journal.com/index.php/journal/article/view/5904/2754
48. repmus.ircam.fr, accessed April 23, 2025, http://repmus.ircam.fr/_media/giavitto/export/q_analysis/laubenbacher_a-homotopy.pdf
49. Topological Signal Processing and Learning: Recent Advances and Future Challenges – arXiv, accessed April 23, 2025, https://arxiv.org/pdf/2412.01576
50. Teaspoon: A Python Package for Topological Signal Processing | Journal of Open Source Software, accessed April 23, 2025, https://joss.theoj.org/papers/10.21105/joss.07243.pdf
51. Topological Signal Processing and Learning: Recent Advances and Future Challenges, accessed April 23, 2025, https://arxiv.org/html/2412.01576v1
52. Topological Data Analysis for Electric Motor Eccentricity Fault Detection, accessed April 23, 2025, https://www.merl.com/publications/docs/TR2022-130.pdf
53. A Robust Topological Framework for Detecting Regime Changes in Multi-Trial Experiments with Application to Predictive Maintenance – arXiv, accessed April 23, 2025, https://arxiv.org/html/2410.20443v1
54. Topological data analysis for true step detection in periodic piecewise constant signals | Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences – Journals, accessed April 23, 2025, https://royalsocietypublishing.org/doi/10.1098/rspa.2018.0027
55. Nonlinear Time Series Analysis and Manifold Learning, accessed April 23, 2025, https://www.oist.jp/course/a111
56. 2.2. Manifold learning — scikit-learn 1.6.1 documentation, accessed April 23, 2025, https://scikit-learn.org/stable/modules/manifold.html
57. Analyzing Deep Transformer Models for Time Series Forecasting via Manifold Learning, accessed April 23, 2025, https://openreview.net/forum?id=FeqxK6PW79
58. [2110.03625] Time Series Forecasting Using Manifold Learning – arXiv, accessed April 23, 2025, https://arxiv.org/abs/2110.03625
59. [2112.03379] Deep Efficient Continuous Manifold Learning for Time Series Modeling – arXiv, accessed April 23, 2025, https://arxiv.org/abs/2112.03379
60. Modelling and simulation of suspension system based on topological structure, accessed April 23, 2025, https://www.researchgate.net/publication/385026746_Modelling_and_simulation_of_suspension_system_based_on_topological_structure
61. A topological graph of the vehicle model. | Download Scientific Diagram – ResearchGate, accessed April 23, 2025, https://www.researchgate.net/figure/A-topological-graph-of-the-vehicle-model_fig1_340324083
62. iVA Intelligent Vibration Analyzer – Automotive Test Solutions, accessed April 23, 2025, https://automotivetestsolutions.com/product/iva-intelligent-vibration-analyzer/
63. Machine Fault Diagnosis through Vibration Analysis: Continuous Wavelet Transform with Complex Morlet Wavelet and Time–Frequency RGB Image Recognition via Convolutional Neural Network – MDPI, accessed April 23, 2025, https://www.mdpi.com/2079-9292/13/2/452
64. Active Suspension Systems | Dorleco, accessed April 23, 2025, https://dorleco.com/active-suspension-systems/
65. Active Suspension Control Using an MPC-LQR-LPV Controller with Attraction Sets and Quadratic Stability Conditions – MDPI, accessed April 23, 2025, https://www.mdpi.com/2227-7390/9/20/2533
66. State Feedback-Based H2 Optimal Control for a Full Vehicle Active Suspension System | Al-Jarrah | International Review of Automatic Control (IREACO) – Praise Worthy Prize, accessed April 23, 2025, https://www.praiseworthyprize.org/jsm/index.php?journal=ireaco&page=article&op=view&path%5B%5D=28329
67. Ride Comfort and Active Suspension Systems towards Automated Driving – An Objective Target Value and a Method to investigate Actuator – mediaTUM, accessed April 23, 2025, https://mediatum.ub.tum.de/doc/1660216/1660216.pdf
68. Human Feedback in AI: How Unit8 Approaches Validation for Reliable AI Systems, accessed April 23, 2025, https://unit8.com/resources/human-feedback-in-ai-how-unit8-approaches-validation-for-reliable-ai-systems/
69. Human-in-the-Loop Machine Learning (HITL) Explained – Encord, accessed April 23, 2025, https://encord.com/blog/human-in-the-loop-ai/
70. [2402.01801] Large Language Models for Time Series: A Survey – arXiv, accessed April 23, 2025, https://arxiv.org/abs/2402.01801
71. Research on robust fault-tolerant control of the controllable suspension based on knowledge-data fusion driven – PMC, accessed April 23, 2025, https://pmc.ncbi.nlm.nih.gov/articles/PMC10733293/
72. Everything You Wanted to Know About LLM Inference Optimization …, accessed April 23, 2025, https://www.tredence.com/blog/llm-inference-optimization
73. Towards Signal Processing In Large Language Models – arXiv, accessed April 23, 2025, https://arxiv.org/html/2406.10254v1
74. The AI Feedback Loop: From Insights to Action in Real-Time, accessed April 23, 2025, https://www.zonkafeedback.com/blog/ai-feedback-loop
75. Continual Human-in-the-Loop Optimization – arXiv, accessed April 23, 2025, https://arxiv.org/html/2503.05405v1
76. Topology Analysis and Structural Optimization of Air Suspension Mechanical-Vibration-Reduction Wheels – MDPI, accessed April 23, 2025, https://www.mdpi.com/2075-1702/12/7/488
77. Sharing reliable information worldwide: healthcare strategies based on artificial intelligence need external validation. Position paper – PubMed Central, accessed April 23, 2025, https://pmc.ncbi.nlm.nih.gov/articles/PMC11796012/
78. Towards Human-AI Synergy in UI Design: Enhancing Multi-Agent Based UI Generation with Intent Clarification and Alignment – arXiv, accessed April 23, 2025, https://arxiv.org/html/2412.20071v1
79. A Novel State Estimation Approach for Suspension System with Time-Varying and Unknown Noise Covariance – MDPI, accessed April 23, 2025, https://www.mdpi.com/2076-0825/12/2/70
80. A Novel State Estimation Approach for Suspension System with Time-Varying and Unknown Noise Covariance – ResearchGate, accessed April 23, 2025, https://www.researchgate.net/publication/368403776_A_Novel_State_Estimation_Approach_for_Suspension_System_with_Time-Varying_and_Unknown_Noise_Covariance