Interpretable Goal Recognition in the Presence of Occluded Factors for Autonomous Vehicles
Open accessAbstract
Recognising the goals or intentions of observed vehicles is a key step towards predicting the long-term future behaviour of other agents in an autonomous driving scenario. When there are unseen obstacles or occluded vehicles in a scenario, goal recognition may be confounded by the effects of these unseen entities on the behaviour of observed vehicles. Existing prediction algorithms that assume rational behaviour with respect to inferred goals may fail to make accurate long-horizon predictions because they ignore the possibility that the behaviour is influenced by such unseen entities. We introduce the Goal and Occluded Factor Inference (GOFI) algorithm which bases inference on inverse-planning to jointly infer a probabilistic belief over goals and potential occluded factors. We then show how these beliefs can be integrated into Monte Carlo Tree Search (MCTS). We demonstrate that jointly inferring goals and occluded factors leads to more accurate beliefs with respect to the true world state and allows an agent to safely navigate several scenarios where other baselines take unsafe actions leading to collisions.
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- Authors
- Hanna, J. P., Rahman, A., Fosong, E., Eiras, F., Dobre, M., Redford, J., Ramamoorthy, S., & Albrecht, S. V.
- Affiliations
- University of Wisconsin–Madison (US)
- University of Edinburgh (GB)
- University of Oxford (GB)
- Venue
- 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Publisher
- IEEE
- Published
- 2021-09-27
- Pages
- 7044-7051
- ISSN
- 2153-0858 (ISSN-L)
- OpenAlex
- W3189552233
- Cited by
- 21
- References
- 33
- Citations by year
- 2025: 2 · 2024: 3 · 2023: 10 · 2022: 4 · 2021: 2
- FWCI
- 4.70
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- UK Research and Innovation
- Royal Society
- University of Edinburgh
- School of Informatics, University of Edinburgh
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Robotic Path Planning Algorithms
- Keywords
- goal recognition, autonomous vehicles, inverse treatment planning, Monte Carlo tree search, intention inference, long-horizon prediction, collision avoidance, multi-agent navigation
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