Jianhao Liu, Leader of Baidu Apollo Cybersecurity, Baidu

Panel moderated by Frank Zhao, Honorable Lifetime President of FISITA, Director of Tsinghua Automotive Strategy Research Institute (TASRI); with Prof. Changjun Wang, Director, Road Transportation Safety Research Center, the Ministry of Public Security of the PRC; Dan Li, VP of FAW R&D Institute, Director of ICV Department; Stanislav Lincer, Functional Safety & Cybersecurity Director, Great Wall Motor Co. Ltd; and Yali Wang, Head of Functional Safety Team in Baidu

The development of autonomous vehicles requires extensive testing of software modules. Developing a reliable software platform which allows testing on a real vehicle is yet a challenging task. Therefore, open-source software platforms are becoming more important for researchers in the field of autonomous driving. For example, Baidu provides the open-source autonomous driving platform Apollo which aims to accelerate testing and deployment of autonomous vehicles. However, the complex software structure hinders an easy integration of developed software modules, especially the motion planning module. Moreover, Baidu's Apollo provides only one possibility to test one's own algorithms in simulation, namely to upload the algorithm in Baidu's cloud platform, which is unacceptable for most autonomous driving companies.

In contrast, the open-source CommonRoad benchmark suites contain diverse testing scenarios, e.g., highway, urban, dense traffic, and interaction with bicyclists and pedestrians. In addition, CommonRoad provides a motion planning framework in Python which enables rapid planner prototyping, along with additional tools, e.g., efficient collision checker, map format converter, and interface with the traffic simulator SUMO.

In this work, we introduce a Python API between the planning module of the Baidu Apollo platform and the CommonRoad software framework. The developed interface aims to bridge the gap between rapid prototyping for safe planning algorithms and real-time test drives. The API transfers perception and map information to the planner and then returns the planned trajectory. The users can either replace the Apollo planner with their own planner or integrate their planner as a fail-safe planner if the planned trajectory by Apollo is unsafe. With our interface, developers can first test their planners in diverse scenarios from the CommonRoad benchmark, and directly on a real vehicle afterwards using the Apollo platform. The latter can be performed without adapting their algorithms to Apollo software structures. Moreover, developers can record their test drives in CommonRoad format for offline analyses. We demonstrate our interface in several scenarios with increasing complexity.

Keywords: Autonomous Driving, Motion Planning, Open-Source Software Framework