Efficient Motion Planning for Automated Lane Change based on Imitation Learning and Mixed-Integer Optimization
This work addresses motion planning for automated vehicles, offering a domain-specific improvement that is incremental in nature.
The paper tackles the problem of efficient and optimal motion planning for automated lane changes by combining imitation learning with mixed-integer optimization, resulting in a model that outperforms baseline methods in optimality, efficiency, and generalization.
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.