Linear Safety Constraints for Urban Automated Driving: a Case Differentiation

Motion planning algorithms for control of automated vehicles in urban scenarios must be provided with safety constraints preventing collisions with traffic participants. Constraints must be designed to handle several different possible traffic scenarios appropriately. In this work, a novel systematic procedure to generate non-conservative linear safety constraints is introduced. We provide a detailed study of possible relative configurations of the automated vehicle with respect to one traffic participant at a time, thus deriving rules for each base case; then, complex traffic scenarios are coped with by combining a limited number of base cases, scaling smoothly if the number of traffic participants increases. The proposed approach can be easily extended to cover new traffic scenarios by adding new base cases. Moreover, such linear constraints result in convex optimization, making the online computation fast. We publish the code to allow testing in reference environments and integration of additional cases.     «

Motion planning algorithms for control of automated vehicles in urban scenarios must be provided with safety constraints preventing collisions with traffic participants. Constraints must be designed to handle several different possible traffic scenarios appropriately. In this work, a novel systematic procedure to generate non-conservative linear safety constraints is introduced. We provide a detailed study of possible relative configurations of the automated vehicle with respect to one traffic p...     »