@article{ref1,
title="A learning-based discretionary lane-change decision-making model with driving style awareness",
journal="IEEE transactions on intelligent transportation systems",
year="2023",
author="Zhang, Yifan and Xu, Qian and Wang, Jianping and Wu, Kui and Zheng, Zuduo and Lu, Kejie",
volume="24",
number="1",
pages="68-78",
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.<p /> <p>Language: en</p>",
language="en",
issn="1524-9050",
doi="10.1109/TITS.2022.3217673",
url="http://dx.doi.org/10.1109/TITS.2022.3217673"
}