Human-like lane-changing trajectory planning algorithm for human–machine conflict mitigation

The purpose of this paper is to alleviate the potential safety problems associated with the human driver and the automatic system competing for the right of way due to different objectives by mitigating the human-machine conflict phenomenon in human-machine shared driving (HMSD) technology from the automation system. Firstly, a basic lane-changing trajectory algorithm based on the quintic polynomial in the Frenet coordinate system is developed. Then, in order to make the planned trajectory close to human behavior, naturalistic driving data is collected, based on which some lane-changing performance features are selected and analyzed. There are three aspects have been taken into consideration for the human-like lane-changing trajectory: vehicle dynamic stability performance, driving cost optimization, and collision avoidance. Finally, the HMSD experiments are conducted with the driving simulator to test the potential of the human-like lane-changing trajectory planning algorithm. The results demonstrate that the lane-changing trajectory planning algorithm with the highest degree of personalization is highly consistent with human driver behavior and consequently would potentially mitigate the human-machine conflict with the HMSD application. Furthermore, it could be further employed as an empirical trajectory prediction result. The algorithm employs the distribution state of the historical trajectory for human-like processing, simplifying the operational process and ensuring the credibility, integrity, and interpretability of the results. Moreover, in terms of optimization processing, the form of optimization search followed by collision avoidance detection is adopted to in principle reduce the calculation difficulty. Additionally, a new convex polygon collision detection method, namely the vertex embedding method, is proposed for collision avoidance detection.