Social navigation framework for autonomous vehicle with hierarchical cyber-physical system architecture

AbstractAn autonomous vehicle operating alongside humans should ideally have a high social capability, such as being able to communicate with humans, negotiate space, predict reactions, etc. This can be achieved by a prediction feature trained with diverse data on human behavior. Hierarchical cyber-physical system (CPS) architecture design, which sends the prediction feature to an external server while observing a limited operational design domain (ODD) and acquiring data continuously, has great potential to refine the training process as well as improve the performance. However, this architecture design requires the planning and prediction modules to be explicitly decoupled, which takes away from the recent success on social navigation. In this paper, we propose a novel autonomous navigation framework enabling social behavior while decoupling the planning and prediction modules to take advantage of the hierarchical CPS architecture. In the proposed framework, pedestrian trajectories are predicted as reactions to pre-generated candidates for an ego vehicle trajectory, and the ego vehicle trajectory is then selected to maximize mutual benefit to both the ego vehicle and surrounding pedestrians. We evaluated the proposed framework with simulations using the social force model and found that it was able to achieve the social behavior.KEYWORDS: Social navigationtrajectory planningcyber-physical systemarchitecture designsocial force model Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsYuto ImanishiYuto Imanishi received his MS degree from the Graduate School of Science and Engineering, Tokyo Institute of Technology, Japan, in 2009. He is currently a senior researcher of Research & Development Group, Hitachi Ltd., Japan. His research interests mainly include autonomous control, cyber-physical systems, and architecture design. He is a member of the SICE and JSAE.Hiroyuki YamadaHiroyuki Yamada received his MS degree and his Ph.D degree from Graduate School of Information Science and Electrical Engineering, Kyushu University, Japan, in 2008 and 2021, respectively. He is currently a senior researcher of Research & Development Group, Hitachi Ltd., Japan. His research interests mainly include robotics, computer vision and machine learning. He is a member of the RSJ and JSME.