LADAR-Based Vehicle Tracking and Trajectory Estimation for Urban Driving

Abstract

Safe mobility for unmanned ground vehicles requires reliable detection of other vehicles, along with precise estimates of their locations and trajectories. Here we describe the algorithms and system we have developed for accurate trajectory estimation of nearby vehicles using an onboard scanning LADAR. We introduce a variable-axis Ackerman steering model and compare this to an independent steering model. Then for robust tracking we propose a multi-hypothesis tracker that combines these kinematic models to leverage the strengths of each. When trajectories estimated with our techniques are input into a planner, they enable an unmanned vehicle to negotiate traffic in urban environments. Results have been evaluated running in real time on a moving vehicle with a scanning LADAR.