Ren, KaiAhn, HeejinKamgarpour, Maryam2022-08-012022-08-012022-08-012022-06-2410.1109/LCSYS.2022.3186269https://infoscience.epfl.ch/handle/20.500.14299/189618WOS:000824789700003We 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.Automation & Control Systemstrajectoryuncertaintytrajectory planningsafetygaussian distributionupper boundprobability distributionautonomous vehiclesstochastic optimal controltext::journal::journal article::research article