New needs to consider during accident analysis: Implications of autonomous vehicles with collision reconfiguration systems

Abstract

Autonomous vehicles are equipped with advanced vehicle technology (AVT) that will improve road traffic safety and reduce accidents. However, due to the uncertain behavior of other road users, collisions can never be completely eliminated. Collision reconfiguration systems offer a solution by, for instance, changing where vehicles are hit and how the impact force is directed towards them. Unfortunately, the logic behind the decision-making of collision reconfiguration systems is fundamentally different from that of other AVTs. Fundamentally different feedback might thus be required from accident analyses to ensure the successful design of collision reconfiguration systems. Through simulations, this study explores decision-making strategies of collision reconfiguration systems to ascertain the implications of which feedback is required from accident analyses. Results show that different strategies can be statistically significantly different from each other in the way they affect severity; and that a new source of unobserved heterogeneity could easily be small variations in the algorithms used by collision reconfiguration systems. Based on this, three new needs to consider during accident analysis are put forth: firstly, new safety surrogate measures (SSMs) that consider severity are required; one such SSM is proposed; secondly, to identify new unobserved heterogeneity as a result of collision reconfiguration systems, the trajectories of traffic near-collisions should be recorded, and statistical tools to identify comparable scenarios developed. Thirdly, new collision patterns will make it difficult to analyze the implications of reconfigured collisions, which suggests that collision configurations must be carefully recorded to provide early feedback.