  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.
