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A Learning-based Discretionary Lane-Change Decision-Making Model with Driving Style Awareness | | Yifan Zhang
; Qian Xu
; Jianping Wang
; Kui Wu
; Zuduo Zheng
; Kejie Lu
; | | Date: |
19 Oct 2020 | | 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 many DLC decision-making
models have been studied in traffic engineering and autonomous driving, the
impact of human factors, which is an integral part of current and future
traffic flow, is largely ignored in the existing literature. In autonomous
driving, the ignorance of human factors of surrounding vehicles will lead to
poor interaction between the ego vehicle and the surrounding vehicles, thus, a
high risk of accidents. The human factors are also a crucial part to simulate a
human-like traffic flow in the traffic engineering area. 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 to the greatest
extent by learning the driving style of the ego vehicle. Our evaluation results
show that the proposed model almost follows the human decision-making
maneuvers, which can achieve 98.66% prediction accuracy with respect to human
drivers’ decisions against the ground truth. Besides, the lane-change impact
analysis results demonstrate that our model even performs better than human
drivers in terms of improving the safety and speed of traffic. | | Source: | arXiv, 2010.09533 | | Services: | Forum | Review | PDF | Favorites | |
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