Classifying and Passing 3D Obstacles for Autonomous Driving

Highly automated driving in real world situations requires perception and planning to account for other forms of human locomotion, principally bicycles, and pedestrians, in addition to vehicles. Here we present a complete perception-planning-control system with high quality perception including object classification and tracking, and a path planning algorithm that makes use of probabilistic classification results. Our classification algorithm is fast enough to provide results for the vast majority of objects in on-line operation, but to ensure output for real-time decision-making, the computation and integration of classification results is done using an anytime algorithm. This work builds on recent research in object classification using LIDAR data (Teichman et. al., 2011). Our focus is on safe driving behavior in the presence of dynamic obstacles on the side of the road, which are typically bicycles, using LIDAR data. Our resulting system is extensively tested in real-world situations, and shows good performance on a large test set of interactions with bicycles.