Stochastic MPC with Multi-modal Predictions for Traffic Intersections

TL;DR

This paper introduces a novel stochastic model predictive control approach for autonomous vehicles at traffic intersections, utilizing multi-modal predictions with Gaussian Mixture Models to improve collision avoidance and reduce conservatism.

Contribution

It presents a new SMPC formulation that exploits structured feedback policies for multi-modal predictions, enabling convex optimization and improved performance in traffic scenarios.

Findings

Enhanced collision avoidance with multi-modal predictions

Reduced conservatism compared to open-loop approaches

Efficient convex optimization implementation

Abstract

We propose a Stochastic MPC (SMPC) formulation for autonomous driving at traffic intersections which incorporates multi-modal predictions of surrounding vehicles for collision avoidance constraints. The multi-modal predictions are obtained with Gaussian Mixture Models (GMM) and constraints are formulated as chance-constraints. Our main theoretical contribution is a SMPC formulation that optimizes over a novel feedback policy class designed to exploit additional structure in the GMM predictions, and that is amenable to convex programming. The use of feedback policies for prediction is motivated by the need for reduced conservatism in handling multi-modal predictions of the surrounding vehicles, especially prevalent in traffic intersection scenarios. We evaluate our algorithm along axes of mobility, comfort, conservatism and computational efficiency at a simulated intersection in CARLA. Our simulations use a kinematic bicycle model and multimodal predictions trained on a subset of the Lyft Level 5 prediction dataset. To demonstrate the impact of optimizing over feedback policies, we compare our algorithm with two SMPC baselines that handle multi-modal collision avoidance chance constraints by optimizing over open-loop sequences.