AI Agents for Vehicle Dynamics Control

This book offers a rigorous, end-to-end treatment of AI agent design for active vehicle safety systems—bridging classical vehicle dynamics theory with modern reinforcement learning, neural operators, and federated learning in a single unified framework. Modern automobiles rely on Electronic Stability Program (ESP), Anti-Lock Braking System (ABS), and Traction Control System (TCS) to keep drivers safe under hazardous conditions. Yet conventional rule-based controllers were designed before the era of deep learning and cannot adapt to the enormous variability of real-world driving: changing road friction, fatigued or distracted drivers, and sub-40 ms ECU pipeline delays that erode control authority precisely when it matters most. This book sets out to solve that gap—replacing static thresholds with perception-aware AI agents that observe, reason, plan, and act within a hard real-time control loop. The core topics span five interconnected areas. First, a control-oriented review of seven-degree-of-freedom vehicle dynamics and Pacejka tyre modelling gives readers the physical foundation needed to evaluate any AI solution critically. Second, a detailed treatment of AI agent architectures—including tool-augmented ReAct agents, Monte Carlo Tree Search planners, and LLM supervisory layers—shows how deliberative reasoning can be embedded alongside 50 Hz reactive control without violating AUTOSAR timing budgets. Third, the novel Fractal Selective State-Space Neural Operator (FSSNO) is introduced for ECU delay compensation in ESP, achieving a 34.7% reduction in yaw-rate tracking error relative to Transformer baselines while meeting the NXP S32G2 hard-deadline constraint. Fourth, a multimodal driver state monitoring system—fusing EMG, pupillometry, and steering entropy signals from a 24-participant, 2,847-minute dataset—feeds driver fatigue and workload estimates directly into the agent's control policy, enabling trust-modulated intervention. Fifth, FedDrive, a federated learning framework with differential privacy (e = 1.2, d = 10?5), addresses the privacy barrier to fleet-wide driver model learning without transmitting raw biometric data.