Real-Time Motion Planning Framework for Autonomous Vehicles with Learned Committed Trajectory Distribution

Abstract

This study proposes a realtime motion planning framework that leverages the prediction of a portion of the optimal trajectory for sampling-based anytime planning algorithms. Existing algorithms predict the entire optimal path and bias random samples toward it for fast path planning. However, these algorithms may not be suitable for realtime frameworks because the bias-sampling strategy should consider the sequential nature of realtime execution. Therefore, the proposed algorithm predicts a portion of the optimal path, known as the committed trajectory, step by step as a probability distribution using a neural network. This distribution is then used in a sampling-based anytime planning algorithm as a non-stationary way of biasing random samples. The proposed algorithm can sequentially plan the near-optimal motion, al-lowing the vehicle to reach the desired goal pose in a timely and accurate manner. In various test parking scenarios, the proposed algorithm reduces the parking time by approximately 38% compared with conventional motion planning algorithms and by 10% compared with another realtime framework that biases samples toward the entire optimal trajectory.