Abstract:
This paper overviews challenges in perception and decision making for intelligent, or highly automated, automotive vehicles. We illustrate our development of a complete p...Show MoreMetadata
Abstract:
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.
Published in: IEEE Transactions on Intelligent Vehicles ( Volume: 1, Issue: 1, March 2016)
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- IEEE Keywords
- Index Terms
- Intelligent Vehicles ,
- Perceptual Challenges ,
- Training Data ,
- Action Plan ,
- Partial Observation ,
- Roundabout ,
- Automated Vehicles ,
- Inverse Reinforcement Learning ,
- Support Vector Machine ,
- Machine Learning Methods ,
- Point Cloud ,
- Autonomous Vehicles ,
- Hidden State ,
- State Representation ,
- State Machine ,
- Speed Limit ,
- Perceptual System ,
- Decision Boundary ,
- Real-world Experiments ,
- Human Operator ,
- Merging Point ,
- Vehicle State ,
- Dynamic Objects ,
- Challenging Scenarios ,
- Prediction Of Formation ,
- Sensor Suite ,
- Joint Representation ,
- Traffic Rules ,
- Robot State ,
- Onboard Sensors
- Author Keywords
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Intelligent Vehicles ,
- Perceptual Challenges ,
- Training Data ,
- Action Plan ,
- Partial Observation ,
- Roundabout ,
- Automated Vehicles ,
- Inverse Reinforcement Learning ,
- Support Vector Machine ,
- Machine Learning Methods ,
- Point Cloud ,
- Autonomous Vehicles ,
- Hidden State ,
- State Representation ,
- State Machine ,
- Speed Limit ,
- Perceptual System ,
- Decision Boundary ,
- Real-world Experiments ,
- Human Operator ,
- Merging Point ,
- Vehicle State ,
- Dynamic Objects ,
- Challenging Scenarios ,
- Prediction Of Formation ,
- Sensor Suite ,
- Joint Representation ,
- Traffic Rules ,
- Robot State ,
- Onboard Sensors
- Author Keywords