Collision Mitigation in the Presence of Uncertainty

Abstract

We present a probabilistic collision mitigation system (CMS) that aims to decrease the severity of unavoidable collisions. CMSs have to decide when to act in order to keep enough trajectories for lowering severity available, while at the same time not increasing the collision probability. This paper operationalises the choice of when to act as a probability threshold, which is applied to all or part of the trajectories available to an automated vehicle equipped with the CMS. Through simulations the relationship between the value of the threshold, and the trade-off between being able to reduce the collision severity and not causing a collision, is investigated. We identify two types of behaviours that increase the severity of collisions: one when acting late (setting a higher threshold) reduces the number of available trajectories with lower severity, and one when acting early is susceptible to prediction errors. Regardless, by setting a lower prediction horizon the prediction accuracy increases, but also reduces the available trajectories with lower severity. We also note that the outcome of the CMS improves when the severity of a trajectory is aligned with the maximum risk of collision along it, rather than the severity at the time step when it crosses the probability threshold.