A Learning-Based Discretionary Lane-Change Decision-Making Model With Driving Style Awareness

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

2 Scopus Citations
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Original languageEnglish
Pages (from-to)68-78
Journal / PublicationIEEE Transactions on Intelligent Transportation Systems
Issue number1
Online published9 Nov 2022
Publication statusPublished - Jan 2023


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. © 2022 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

Research Area(s)

  • autonomous driving, decision-making model, Discretionary lane change, driving style

Citation Format(s)