Modeling of Intelligent Vehicle Longitudinal Dynamics and Control System Design

This project focuses on establishing intelligent vehicle longitudinal dynamic models and designing robust control systems for autonomous driving functionality. The modeling process incorporates critical factors including acceleration dynamics, braking performance, and steering interactions, forming a comprehensive vehicle dynamics framework. The control system architecture employs sensor fusion algorithms to process real-time vehicle state data and target trajectory information, generating precise acceleration and braking commands through PID controllers with feedforward compensation. Key implementation aspects include state-space modeling for vehicle longitudinal motion, Kalman filtering for state estimation, and MPC (Model Predictive Control) algorithms for optimal actuation. The system ensures stable cruising and precise maneuverability by continuously optimizing control parameters based on vehicle dynamics feedback. This research contributes to enhanced performance and safety metrics for intelligent vehicles, advancing the development of future transportation systems through validated simulation models and hardware-in-loop testing methodologies.