Challenges in Perception and Decision Making for Intelligent Automotive Vehicles: A Case Study
| Autor: | Michael James, Bunyo Okumura, Tomoki Nishi, Katsuhiro Sakai, Yusuke Kanzawa, Matthew O. Derry, Danil V. Prokhorov |
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| Rok vydání: | 2016 |
| Předmět: |
Engineering
Control and Optimization business.industry media_common.quotation_subject Automotive industry Intelligent decision support system Interdependence Risk analysis (engineering) Artificial Intelligence Perception Automotive Engineering Automated planning and scheduling Roundabout Observability Artificial intelligence business Classifier (UML) media_common |
| Zdroj: | IEEE Transactions on Intelligent Vehicles. 1:20-32 |
| ISSN: | 2379-8904 2379-8858 |
| DOI: | 10.1109/tiv.2016.2551545 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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