The Study on the Co-Optimization of Guidance Decision-Making and Chassis Control for Self-Driving Vehicles

In the context of rapidly developing Autonomous Driving Technology, the realization of efficient and safe Autonomous Driving has become a research hotspot. This paper focuses on the cooperative optimization of guidance decision-making and chassis control for self-driving vehicles to develop a comprehensive review. The study begins with the importance of planning vehicle driving paths and managing traffic scenarios, and then delves into the importance of chassis control in executing commands and preserving vehicle dynamics. The section on current research includes cooperative methods based on model predictive control, hierarchical control, and other advanced control strategies. This section also includes an analysis of the effectiveness of these methods in enhancing vehicle stability, maneuverability, and energy efficiency. The text then examines the challenges faced during cooperative optimization, such as strong multi-system coupling, high real-time requirements, and adaptability to complex environments. It ends by summarizing possible solutions and discussing future developments, such as integrating AI and machine learning in optimization, and expanding it to networked and intelligent traffic environments, among others. These developments are discussed to provide valuable references for further breakthroughs and applications of self-driving vehicle-related technologies, and for promoting self-driving technology to move forward to a higher level.