Challenges in Perception and Decision Making for Intelligent Automotive Vehicles: A Case Study

• Published in: IEEE transactions on intelligent vehicles 2016-03, Vol.1 (1), p.20-32
• Main Authors: Okumura, Bunyo, James, Michael R., Kanzawa, Yusuke, Derry, Matthew, Sakai, Katsuhiro, Nishi, Tomoki, Prokhorov, Danil
• Format: Article
• Language: English
• Subjects: Automata; Autonomous driving; classifier; Decision making; finite state machine; high-definition lidar; high-fidelity map; Laser radar; learning from demonstration; Machine learning; robot; Robot sensing systems; roundabout; state representation; Support vector machine classification
• ISSN: 2379-8858; 2379-8904
• DOI: 10.1109/TIV.2016.2551545

This paper overviews challenges in perception and decision making for intelligent, or highly automated, automotive vehicles. We illustrate our development of a complete perception and decision making system which addresses various challenges and propose an action planning method for highly automated vehicles which can merge into a roundabout. We use learning from demonstration to construct a classifier for high-level decision making, and develop a novel set of formulations that is suited to this challenging situation: multiple agents in a highly dynamic environment with interdependencies between agents, partial observability, and a limited amount of training data. Having limited amount of labeled training data is highly constraining, but a very real issue in real-world applications. We believe that our formulations are also well suited to other automated driving scenarios.