Work

Learning-Based Stochastic Model Predictive Control for Autonomous Driving at Uncontrolled Intersections

Year: 2024

Type: article

Abstract: 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.

Source: IEEE Transactions on Intelligent Transportation Systems

DOI: 10.1109/tits.2024.3510041

Authors: Surya Soman, Mario Zanon, Alberto Bemporad

Institution: IMT School for Advanced Studies Lucca

Language: English

Topic: Traffic control and management

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: hybrid

Estimated APC paid: $2,495