Generation of Reference Trajectories for Safe Trajectory Planning

Many variants of a sampling-based motion planning algorithm, namely Rapidly-exploring Random Tree, use biased-sampling for faster convergence. One of such recently proposed variant, the Hybrid-Augmented CL-RRT+, uses a predicted predefined template trajectory with a machine learning algorithm as a reference for the biased sampling. Because of the finite number of template trajectories, the convergence time is short only in scenarios where the final trajectory is close to predicted template trajectory. Therefore, a generative model using variational autoencoder for generating many reference trajectories and a 3D-ConvNet regressor for predicting those reference trajectories for critical vehicle traffic-scenarios is proposed in this work. Using this framework, two different safe trajectory planning algorithms, namely GATE and GATE-ARRT+, are presented in this paper. Finally, the simulation results demonstrate the effectiveness of these algorithms for the trajectory planning task in different types of critical vehicle traffic-scenarios.