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 (ε = 1.2, δ = 10-»-µ), addresses the privacy barrier to fleet-wide driver model learning without transmitting raw biometric data.
