Motion planning for autonomous vehicles in highly constrained urban environments

Abstract

In this paper, we present a motion planning algorithm for autonomous navigation in highly constrained urban environments. Since common approaches to on-road trajectory planning turned out to be unsuitable for this task, we instead extended an A*-based planner originally designed for navigation in unstructured environments. Two novel node expansion methods were added to obtain smooth and accurate trajectories that consider the structure of the environment. The first one attempts to find a trajectory connecting the current node directly to the goal by solving a boundary value problem using numerical optimization. The second method leverages a simulated pure-pursuit controller to generate edges (i.e. short motion primitives) that guide the vehicle toward or along the global reference path. As a result, the planner is able to produce smooth paths while retaining the explorative power of A* that is needed to deal with challenging situations in urban driving (e.g., reversing in order to pass a vehicle that stopped unexpectedly). Its practical usefulness was demonstrated during extensive tests on an electric vehicle navigating a mock urban environment as well as on our own autonomous vehicle MuCAR-3.