Clustering obstacle predictions to improve contingency planning for autonomous road vehicles in congested environments

Abstract

A hierarchical trajectory clustering algorithm is presented with the goal of clustering a set of mutually exclusive obstacle trajectory predictions for use in a contingency based path planner for an autonomous road vehicle. This clustering algorithm improves the computational scaling of the contingency planner by limiting the total number of required contingency paths while preserving the performance advantages of exhaustive contingency planning. This algorithm seeks to maximize dissimilarity between trajectory clusters with regard to their potential effect on a robot's future path. Simulation results show that the clustering algorithm allows a robot to maintain many of the benefits of contingency planning while requiring fewer contingency paths.