On worst case performance of collision avoidance systems

Automotive Collision Avoidance and Mitigation (CA/CM) systems help drivers to avoid collisions through autonomous interventions by braking or steering. If the decision to intervene is made too early, the intervention can become a nuisance to the driver and if the decision is made too late, the safety benefits of the intervention will be reduced. Decision timing is thus crucial for the successful operation of a CA/CM system. The decision to intervene is commonly taken when a threat function reaches a specific threshold. The dimensionality of the input state space for the threat function is in general very large making exhaustive evaluation in real vehicles expensive and time consuming. This paper presents a method for efficient estimation of a lower bound on CA/CM system performance, i.e. the worst case performance. The method is applied on an example system for a set of longitudinal single object escape scenarios. Results show significant variation in worst case decision timing across scenarios.