This paper presents a novel optimization-based path planner that is capable of planning multiple contingency paths to directly account for uncertainties in the future trajectories of dynamic obstacles. This planner addresses the particular problem of probabilistic collision avoidance for autonomous road vehicles that are required to safely interact, in close proximity, with other vehicles with unknown intentions. The presented path planner utilizes an efficient spline-based trajectory representation and fast but accurate collision probability bounds to simultaneously optimize multiple continuous contingency paths in real time. These collision probability bounds are efficient enough for real-time evaluation, yet accurate enough to allow for practical close-proximity driving behaviors such as passing an obstacle vehicle in an adjacent lane. An obstacle trajectory clustering algorithm is also presented to enable the path planner to scale to multiple-obstacle scenarios. Simulation results show that the contingency planner allows for a more aggressive driving style than planning a single path without compromising the overall safety of the robot.

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.

This paper presents a novel optimization based path planner that can simultaneously plan multiple contingency paths to account for the uncertain actions of dynamic obstacles. This planner addresses the particular problem of collision avoidance for autonomous road vehicles which are required to safely interact with other vehicles with unknown intentions. The presented path planner utilizes an efficient spline based trajectory representation and fast but accurate collision probability approximations to enable the simultaneous optimization of multiple contingency paths.