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Rationale-aware Autonomous Driving Policy utilizing Safety Force Field implemented on CARLA Simulator
Ho Suk · Taewoo Kim · Hyungbin Park · PAMUL YADAV · Junyong Lee · Shiho Kim

Despite the rapid improvement of autonomous driving technology in recent years, automotive manufacturers must resolve liability issues to commercialize autonomous passenger car of SAE J3016 Level 3 or higher. To cope with the product liability law, manufacturers develop autonomous driving systems in compliance with international standards for safety such as ISO 26262 and ISO 21448. Concerning the safety of the intended functionality (SOTIF) requirement in ISO 26262, the driving policy recommends providing an explicit rational basis for maneuver decisions. In this case, mathematical models such as Safety Force Field (SFF) and Responsibility-Sensitive Safety (RSS) which have interpretability on decision, may be suitable. In this work, we implement SFF from scratch to substitute the undisclosed NVIDIA's source code and integrate it with CARLA open-source simulator. Using SFF and CARLA, we present a predictor for claimed sets of vehicles, and based on the predictor, propose an integrated driving policy that consistently operates regardless of safety conditions it encounters while passing through dynamic traffic. The policy does not have a separate plan for each condition, but using safety potential, it aims human-like driving blended in with traffic flow.

Author Information

Ho Suk (Yonsei University)
Taewoo Kim (Yonsei University)
Hyungbin Park (Yonsei University)
PAMUL YADAV (Yonsei University)
Junyong Lee (Yonsei university)
Shiho Kim (Yonsei University)

Dr. Shiho Kim is directing Seamless Transportation Lab(http://sites.google.com/site/shihoyonsei/home) and VR Lab (vr.yonsei.ac.kr)in Yonsei Institute of Convergence Technology. He found Coperative Autonomous Vehicle research Center, a research center for development of autonomous vehicle and Intelligent Transportation System(ITS) supported SKT in 2017. His main research interest includes intelligent vehicles, Reinforcement learning, and virtual reality.

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