Design and Optimization of Safe and Efficient Human-Machine Collaborative Autonomous Driving Systems: Addressing Challenges in Interaction, System Downgrade, and Driver Intervention

In the realm of intelligent vehicles, the collaborative driving mode between autonomous systems and human drivers is regarded as a long-term developmental trend. This paradigm integrates autonomous systems, drivers, and road environments, necessitating the design of a safe and efficient human-machine co-driving system due to its inherent complexity. This study delves into the interaction mechanisms of human-machine co-driving, clarifies the types of driver takeover scenarios, and thoroughly examines the multifaceted challenges encountered during system downgrade, including the accuracy of motion prediction, driving risk assessment, driver state monitoring, vehicle state information analysis, and the optimization of human-machine interaction interfaces. To address these challenges, we propose a series of design recommendations: enhancing the efficacy of autonomous driving systems through dimensionality reduction strategies; bolstering system robustness with redundant environmental perception; constructing advanced prediction and risk assessment models; establishing a comprehensive driver state evaluation system; optimizing vehicle state information analysis during takeover processes; and designing intuitive human-machine interaction interfaces. The aim is to develop a co-driving system that not only leverages the strengths of autonomous driving but also ensures the effective intervention of drivers when necessary, thereby providing robust theoretical support and practical guidance for the construction of safe, efficient, and intelligent autonomous driving human-machine collaborative systems.