Citation

Zhang Y, Xu Q, Wang J, Wu K, Zheng Z, Lu K. IEEE Trans. Intel. Transp. Syst. 2023; 24(1): 68-78.

Copyright

(Copyright © 2023, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TITS.2022.3217673

PMID

unavailable

Abstract

Discretionary lane change (DLC) is a basic but complex maneuver in driving, which aims at reaching a faster speed or better driving conditions, e.g., further line of sight or better ride quality. Although modeling DLC decision-making has been studied for years, the impact of human factors, which is crucial in accurately modelling human DLC decision-making strategies, is largely ignored in the existing literature. In this paper, we integrate the human factors that are represented by driving styles to design a new DLC decision-making model. Specifically, our proposed model takes not only the contextual traffic information but also the driving styles of surrounding vehicles into consideration and makes lane-change/keep decisions. Moreover, the model can imitate human drivers' decision-making maneuvers by learning the driving style of the ego vehicle. Our evaluation results show that the proposed model captures the human decision-making strategies and imitates human drivers' lane-change maneuvers, which can achieve 98.66% prediction accuracy. Moreover, we also analyze the lane-change impact of our model compared with human drivers in terms of improving the safety and speed of traffic.

Language: en

Keywords

Analytical models; autonomous driving; Computational modeling; Decision making; decision-making model; Discretionary lane change; driving style; Human factors; Mathematical models; Predictive models; Vehicles