Equivariant Deep Learning of Mixed-Integer Optimal Control Solutions for Vehicle Decision Making and Motion Planning

TL;DR

This paper introduces a neural network-based approach for real-time vehicle decision making and motion planning by predicting MIQP solutions, improving speed and safety in autonomous driving scenarios.

Contribution

It presents a novel equivariant deep learning model that predicts MIQP solutions for vehicle planning, incorporating a feasibility projector to enhance safety and feasibility.

Findings

Achieves real-time decision making in complex traffic scenarios

Improves collision avoidance and trajectory feasibility

Demonstrates effectiveness in SUMO simulations

Abstract

Mixed-integer quadratic programs (MIQPs) are a versatile way of formulating vehicle decision making and motion planning problems, where the prediction model is a hybrid dynamical system that involves both discrete and continuous decision variables. However, even the most advanced MIQP solvers can hardly account for the challenging requirements of automotive embedded platforms. Thus, we use machine learning to simplify and hence speed up optimization. Our work builds on recent ideas for solving MIQPs in real-time by training a neural network to predict the optimal values of integer variables and solving the remaining problem by online quadratic programming. Specifically, we propose a recurrent permutation equivariant deep set that is particularly suited for imitating MIQPs that involve many obstacles, which is often the major source of computational burden in motion planning problems. Our framework comprises also a feasibility projector that corrects infeasible predictions of integer variables and considerably increases the likelihood of computing a collision-free trajectory. We evaluate the performance, safety and real-time feasibility of decision-making for autonomous driving using the proposed approach on realistic multi-lane traffic scenarios with interactive agents in SUMO simulations.