The DOI matches its Crossref record; 17 of the 17 references it cites were found in a registry or an index.
Behavior and Interaction-aware Motion Planning for Autonomous Driving Vehicles based on Hierarchical Intention and Motion Prediction
Abstract
Safe motion planning in complex and interactive environments is one of the major challenges for developing autonomous vehicles. In this paper, we propose an interaction and behavior-aware motion planning framework based on joint predictions of intentions and motions of surrounding vehicles. A multimodal hierarchical Inverse Reinforcement Learning (IRL) framework is designed to learn joint driving pattern-intention-motion models from real-world interactive driving trajectory data. Using the learned models, the proposed approach can probabilistically predict the continuous motions partitioned by discrete driving styles and intentions, while taking into account of the interactions between participants' actions. A chance-constrained POMDP strategy is utilized to generate risk-bounded motion plans based on those predictions. Simulation case studies based on challenging scenarios demonstrate that the proposed framework is effective in predicting surrounding vehicles' behaviors and generating safe motion plans in complex traffic environments.
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A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles
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Continuous Inverse Optimal Control with Locally Optimal ExamplesPreprint
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An Online POMDP Solver for Uncertainty Planning in Dynamic Environment
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Apprenticeship Learning About Multiple Intentions
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Chen, S., Peng, C., Yang, B., Lin, J., Zhou, L., Jiang, Z., Liu, Z., Liu, Y., & Tang, L. (2026) · npj Artificial Intelligence · doi:10.1038/s44387-026-00083-5 · Cited by 9
Metadata
- Authors
- Li, D., Wu, Y., Bai, B., & Hao, Q.
- Affiliations
- Southern University of Science and Technology (CN)
- Venue
- 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)
- Publisher
- IEEE
- Published
- 2020-09-20
- Pages
- 1-8
- OpenAlex
- W3115190193
- Cited by
- 10
- References
- 17
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 3 · 2023: 1 · 2022: 1 · 2021: 1
- FWCI
- 0.62
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE International Conference on Intelligent Transportation Systems (ITSC) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
- Physical Sciences › Engineering › Control and Systems Engineering › Robotic Path Planning Algorithms
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Traffic and Road Safety
- Keywords
- autonomous driving, motion planning, intention prediction, motion prediction, multimodal learning, risk-aware planning, vehicle interaction
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