Autonomous driving in urban environments requires safe control policies that account for the non-determinism of moving obstacles, such as the position other vehicles will take while crossing an uncontrolled intersection. We address this problem by proposing a stochastic model predictive control (MPC) approach with robust collision avoidance constraints to guarantee safety. By adopting a stochastic formulation, the quality of closed-loop tracking is increased by avoiding giving excessive importance to future obstacle configurations that are unlikely to occur. We compute the probabilities associated with different obstacle trajectories by learning a classifier on a realistic dataset generated by the microscopic traffic simulator SUMO and show the benefits of the proposed stochastic MPC formulation on a simulated realistic intersection.
Learning-Based Stochastic Model Predictive Control for Autonomous Driving at Uncontrolled Intersections
IEEE Transactions on Intelligent Transportation Systems ; 26 , 2 ; 1538-1546
2025-02-01
6556305 byte
Article (Journal)
Electronic Resource
English
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