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

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.

References in corpus (7)

• Adam: A Method for Stochastic Optimization
• Search-based optimal motion planning for automated driving
• Dynamic Input for Deep Reinforcement Learning in Autonomous Driving
• Optimal Behavior Planning for Autonomous Driving: A Generic Mixed-Integer Formulation
• Linear Differential Games for Cooperative Behavior Planning of Autonomous Vehicles Using Mixed-Integer Programming
• Efficient Motion Planning for Automated Lane Change based on Imitation Learning and Mixed-Integer Optimization
• Real-time Mixed-Integer Quadratic Programming for Vehicle Decision Making and Motion Planning

Cited by in corpus (2)

• A Mixed-Integer Approach for Motion Planning of Nonholonomic Robots under Visible Light Communication Constraints
• On Kinodynamic Global Planning in a Simplicial Complex Environment: A Mixed Integer Approach