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Chance-Constrained Trajectory Planning With Multimodal Environmental Uncertainty
Cited 8 time in
Web of Science
Cited 9 time in Scopus
- Authors
- Issue Date
- 2022-06
- Citation
- IEEE Control Systems Letters, Vol.7, pp.13-18
- Abstract
- We tackle safe trajectory planning under Gaussian mixture model (GMM) uncertainty. Specifically, we use a GMM to model the multimodal behaviors of obstacles' uncertain states. Then, we develop a mixed-integer conic approximation to the chance-constrained trajectory planning problem with deterministic linear systems and polyhedral obstacles. When the GMM moments are estimated via finite samples, we develop a tight concentration bound to ensure the chance constraint with a desired confidence. Moreover, to limit the amount of constraint violation, we develop a Conditional Value-at-Risk (CVaR) approach corresponding to the chance constraints and derive a tractable approximation for known and estimated GMM moments. We verify our methods with state-of-the-art trajectory prediction algorithms and autonomous driving datasets.
- ISSN
- 2475-1456
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