Analysis of implicit communication of motorists and cyclists in intersection using video and trajectory data

Kurzfassung

The interaction of automated vehicles with vulnerable road users is one of the greatest challenges in the development of automated driving functions. In order to improve efficiency and ensure the safety of mixed traffic, automated driving functions need to understand the intention of vulnerable road users, to adapt 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 automated driving functions. The aim of this study is to identify the patterns of implicit communication between human drivers and 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 was recorded at an intersection in Braunschweig. Data had been recorded for four weeks: from 22nd August to 18th September 2016. 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, four common interaction patterns between motorist and cyclist when approaching the intersection were identified. With regard to their relative position: 1. Motorists were in front of cyclists at all times; 2. Motorist were in front, then abreast then, and in the end behind cyclists; 3. Motorists stayed behind cyclists; 4. Motorists were behind, then abreast, and again behind cyclist. The analysis of the trajectory data revealed different patterns of changes in velocity and the interdependence of the spatial distance. The findings of this study might provide automated driving functions with a communication strategy, contributing to traffic efficiency as well as ensuring safety in the interaction with vulnerable road users. In a next step, suitable parameters need to be identified in order to quantify the performance of the communication.