Efficient Motion Planning for Automated Lane Change based on Imitation Learning and Mixed-Integer Optimization

Abstract

Intelligent motion planning is one of the core components in automated vehicles, which has received extensive interests. Traditional motion planning methods suffer from several drawbacks in terms of optimality, efficiency and generalization capability. Sampling based methods cannot guarantee the optimality of the generated trajectories. Whereas the optimization-based methods are not able to perform motion planning in real-time, and limited by the simplified formalization. In this work, we propose a learning-based approach to handle those shortcomings. Mixed Integer Quadratic Problem based optimization (MIQP) is used to generate the optimal lane-change trajectories which served as the training dataset for learning-based action generation algorithms. A hierarchical supervised learning model is devised to make the fast lane-change decision. Numerous experiments have been conducted to evaluate the optimality, efficiency, and generalization capability of the proposed approach. The experimental results indicate that the proposed model outperforms several commonly used motion planning baselines.

arXiv commentAccepted by IEEE ITSC 2020

References in corpus (2)

• Congested Traffic States in Empirical Observations and Microscopic Simulations
• A Data Driven Method of Optimizing Feedforward Compensator for Autonomous Vehicle

Cited by in corpus (3)

• Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Traffic
• Driving Decision and Control for Autonomous Lane Change based on Deep Reinforcement Learning
• Equivariant Deep Learning of Mixed-Integer Optimal Control Solutions for Vehicle Decision Making and Motion Planning