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A self-learning lane change motion planning system considering the driver’s personality
Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering ( IF 1.5 ) Pub Date : 2021-04-21 , DOI: 10.1177/09544070211010598
Zhenhai Gao 1 , Naixuan Zhu 1 , Fei Gao 1 , Xingtai Mei 2 , Bin Yang 1
Affiliation  

Nowadays, with more and more attention being paid to the characteristics and experience of drivers, a large number of driver classification algorithms have emerged. However, these methods basically cannot be adjusted independently to each driver. Therefore, this paper proposes a self-learning lane change motion planning system considering the driver’s personality. Firstly, the method of driver data acquisition and processing is determined to obtain and extract the lane change data. Then, the planning system built in this paper is explained from two aspects: lane change trigger and lane change trajectory. According to the artificial potential field theory, an obstacle driving risk field is established to evaluate the acceptance of environmental risks of different drivers, and to achieve personalized lane change triggers through online statistics. At the same time, the safety of lane change is ensured by establishing the safety distance model of the target lane. On the other hand, the driver characteristic coefficient Jc and the driver reaction and operation time td are introduced into the traditional Gaussian-distributed model to establish a personalized lane change trajectory planning model, in which the parameters are obtained from offline and online learning. Offline learning is based on DTW for trajectory matching, and uses AP clustering to obtain the generalized parameters; Online learning uses LSTM to achieve personalized updates. Finally, this paper selected 15 drivers for verification, and the results show that the motion planning system can well reproduce the lane change behavior of the driver.



中文翻译:

考虑驾驶员个性的自学车道变更运动计划系统

如今,随着驾驶员的特性和经验越来越受到关注,涌现出了大量的驾驶员分类算法。但是,这些方法基本上不能独立于每个驱动程序进行调整。因此,本文提出一种考虑驾驶员个性的自学车道变更运动计划系统。首先,确定驾驶员数据的获取和处理方法以获得和提取车道变更数据。然后,从换道触发和换道两个方面对本文构建的规划系统进行了说明。根据人工势场理论,建立了障碍物驱动风险域,以评估不同驾驶员对环境风险的接受程度,并通过在线统计实现个性化的车道变更触发。同时,通过建立目标车道的安全距离模型来确保车道变换的安全性。另一方面,驾驶员特性系数将J c以及驾驶员的反应和操作时间t d引入传统的高斯分布模型中,以建立个性化的车道变化轨迹规划模型,该模型的参数是通过离线和在线学习获得的。离线学习基于DTW进行轨迹匹配,并使用AP聚类获得广义参数。在线学习使用LSTM实现个性化更新。最后,本文选择了15个驾驶员进行验证,结果表明运动规划系统可以很好地重现驾驶员的换道行为。

更新日期:2021-04-21
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