This paper describes a stochastic predictive control algorithm for partially observable Markov decision processes (POMDPs) with time-joint chance constraints. We first present the algorithm as a general tool to treat finite space POMDP problems with time-joint chance constraints together with its theoretical properties. We then discuss its application to autonomous vehicle control on highways. In particular, we model decision-making/behavior-planning for an autonomous vehicle accounting for safety in a dynamic and uncertain environment as a constrained POMDP problem and solve it using the proposed algorithm. After behavior is planned, we use nonlinear model predictive control (MPC) to execute the behavior commands generated from the planner. This two-layer control framework is shown to be effective by simulations.
Stochastic Predictive Control for Partially Observable Markov Decision Processes With Time-Joint Chance Constraints and Application to Autonomous Vehicle Control
Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received April 10, 2018; final manuscript received February 21, 2019; published online March 27, 2019. Assoc. Editor: Vladimir Vantsevich.
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Li, N., Girard, A., and Kolmanovsky, I. (March 27, 2019). "Stochastic Predictive Control for Partially Observable Markov Decision Processes With Time-Joint Chance Constraints and Application to Autonomous Vehicle Control." ASME. J. Dyn. Sys., Meas., Control. July 2019; 141(7): 071007. doi: https://doi.org/10.1115/1.4043115
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