Zhang Meng, Dotzauer Mandy, Schießl Caroline
Institute of Transportation Systems, German Aerospace Center (DLR), Berlin, Germany.
Institute of Transportation Systems, German Aerospace Center (DLR), Braunschweig, Germany.
Front Psychol. 2022 Apr 25;13:864488. doi: 10.3389/fpsyg.2022.864488. eCollection 2022.
The interaction of automated vehicles with vulnerable road users is one of the greatest challenges in the development of automated driving functions (ADF). In order to improve efficiency and ensure the safety of mixed traffic, ADF need to understand the intention of vulnerable road users, to adapt to their driving behavior, and to show its intention. However, this communication may occur in an implicit way, meaning they may communicate with vulnerable road users by using dynamic information, such as speed, distance, etc. Therefore, investigating patterns of implicit communication of human drivers with vulnerable road users is relevant for developing ADF. The aim of this study is to identify the patterns of implicit communication of human drivers with vulnerable road users. For this purpose, the interaction between right-turning motorists and crossing cyclists was investigated at a traffic light controlled urban intersection. In the scenario, motorists and cyclists had a green signal at the same time, but cyclist had right-of-way. Using the Application Platform for Intelligent Mobility (AIM) Research Intersection, trajectory and video data were recorded at an intersection in Braunschweig, Germany. Data had been recorded for 4 weeks. Based on the criticality metric post-encroachment time (PET) and quality of the recorded trajectory, 206 cases of interaction were selected for further analyses. According to the video annotation, when approaching the intersection, three common communication patterns were identified: (1) no yield, motorists, who should yield to cyclists, crossed the intersection first while forcing right-of-way; (2) active yield, motorists, who were in front of cyclists, gave the right-of-way; (3) passive yield, motorists, who were behind cyclists, had to give the right-of-way. The analysis of the trajectory data revealed different patterns of changes in time advantage in these three categories. Additionally, the communication patterns were evaluated with regard to frequency of occurrence, efficiency, and safety. The findings of this study may provide knowledge for the implementation of a communication strategy for ADF, contributing to traffic efficiency as well as ensuring safety in the interaction with vulnerable road users.
自动驾驶车辆与易受伤害道路使用者之间的交互是自动驾驶功能(ADF)发展过程中面临的最大挑战之一。为了提高效率并确保混合交通的安全,ADF需要理解易受伤害道路使用者的意图,适应他们的驾驶行为,并表明自身意图。然而,这种沟通可能以一种隐含的方式进行,也就是说它们可能通过速度、距离等动态信息与易受伤害道路使用者进行沟通。因此,研究人类驾驶员与易受伤害道路使用者之间的隐含沟通模式对于开发ADF具有重要意义。本研究的目的是识别人类驾驶员与易受伤害道路使用者之间的隐含沟通模式。为此,在一个交通信号灯控制的城市十字路口对右转机动车驾驶员和横穿马路的自行车骑行者之间的交互进行了调查。在该场景中,机动车驾驶员和自行车骑行者同时获得绿灯信号,但自行车骑行者具有优先通行权。利用智能移动应用平台(AIM)研究十字路口,在德国不伦瑞克的一个十字路口记录了轨迹和视频数据。数据记录了4周时间。基于临界指标侵入后时间(PET)和记录轨迹的质量,选择了206个交互案例进行进一步分析。根据视频标注,在接近十字路口时,识别出三种常见的沟通模式:(1)不让行,本应让行自行车骑行者的机动车驾驶员在强行夺取优先通行权的同时率先穿过十字路口;(2)主动让行,在自行车骑行者前方的机动车驾驶员让出优先通行权;(3)被动让行,在自行车骑行者后方的机动车驾驶员不得不让出优先通行权。对轨迹数据的分析揭示了这三类中时间优势的不同变化模式。此外,还从出现频率、效率和安全性方面对沟通模式进行了评估。本研究的结果可能为ADF沟通策略的实施提供知识,有助于提高交通效率,并确保与易受伤害道路使用者交互时的安全。