Multistage Stochastic Model Predictive Control for Urban Automated Driving

TL;DR

This paper introduces a stochastic model predictive control framework with a hierarchical structure for urban automated driving, balancing safety and efficiency by planning maneuvers and trajectories separately under uncertainty.

Contribution

It proposes a novel two-stage hierarchical stochastic MPC approach for urban driving, combining long-horizon maneuver planning with detailed trajectory execution.

Findings

Numerical simulations demonstrate the effectiveness of the proposed method.

The approach maintains low risk probability while optimizing trajectories.

Hierarchical planning improves computational efficiency and safety.

Abstract

Trajectory planning in urban automated driving is challenging because of the high uncertainty resulting from the unknown future motion of other traffic participants. Robust approaches guarantee safety, but tend to result in overly conservative motion planning. Hence, we propose to use Stochastic Model Predictive Control for vehicle control in urban driving, allowing to efficiently plan the vehicle trajectory, while maintaining the risk probability sufficiently low. For motion optimization, we propose to use a two-stage hierarchical structure that plans the trajectory and the maneuver separately. A high-level layer takes advantage of a long prediction horizon and of an abstract model to plan the optimal maneuver, and a lower level is in charge of executing the selected maneuver by properly planning the vehicle's trajectory. Numerical simulations are included, showing the potential of our proposal.