Automatic Lane-Changing Trajectory Planning: From Self-Optimum to Local-Optimum

• Published in: IEEE transactions on intelligent transportation systems Vol. 23; no. 11; pp. 21004 - 21014
• Main Authors: Li, Yang, Li, Linbo, Ni, Daiheng, Wang, Wenxuan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Behavioral sciences; Driving; HighD dataset; Lane changing; Lane-changing maneuver; lane-changing trajectory planning; Liquid crystal displays; Longitudinal control; longitudinal control model; Planning; Safety; Smoothing methods; Traffic speed; Trajectory; Trajectory optimization; Trajectory planning; Vehicles
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2022.3179117

Existing lane-changing (LC) algorithms in general prioritize self-interest above the benefits of others. We argue that an autonomous vehicle should be socially responsible during lane change without causing excessive impact on its surroundings. Thus, in this paper, we propose an LC algorithm that could generate a trajectory to optimize the overall benefits of the subject vehicle and others in its vicinity. This is mainly achieved by introducing Longitudinal Control Model to characterize the driving behaviors of neighboring vehicles, and drivers' needs such as comfort, efficiency, and safety are simultaneously satisfied. We combine macro and micro comparative analysis to evaluate the advantage of our approach against existing algorithms. Our findings reveal that the vehicle controlled by our algorithm is capable of safely performing the LC maneuver while causing the least amount of impact. This is reflected in the decrease in total cost of all vehicles and the increase in traffic speed and throughput in its vicinity. In addition, we use HighD dataset for further validation and demonstrate that our algorithm is practically sound. Application of this research can lead to improved autonomous driving technology.