Collision Avoidance Effectiveness of an Automated Driving System Using a Human Driver Behavior Reference Model in Reconstructed Fatal Collisions

Avoiding and mitigating any potential collision is dependent on (1) road user ability to avoid entering into a conflict, the (conflict avoidance effect), and (2) road user response should a conflict be entered, the (collision avoidance effect). This study examined the collision avoidance effect of an automated driving system (ADS) using a human behavior reference model. The reference model used in this study reflects the response time and evasive action (referred to as collision avoidance) of a human driver that is non-impaired, with eyes on the conflict (NIEON) - a consistently performing, always-attentive driver that does not exist in the human population. Notably, the NIEON model is a tool for evaluating collision avoidance only, as it inherits the pre-conflict behaviors (includes conflict avoidance) of the ADS that is being evaluated. Counterfactual simulations were run on responder collision scenarios based on reconstructions from a 10-year period of human fatal crashes from the Operational Design Domain of the Waymo ADS in Chandler, Arizona. Of 16 simulated conflicts entered, 12 (75%) were prevented by the Waymo Driver, and 10 (62.5%) were prevented by the NIEON model. The NIEON Model mitigated an additional 5 collisions and did not mitigate 1 collision. In these 16 conflicts entered, 93% of serious injury risk was reduced by the Waymo Driver, whereas 84% of serious injury risk was reduced by the NIEON model. Further, in a case-by-case evaluation, the Waymo Driver’s collision avoidance led to reduced serious injury risk when compared to the NIEON model in every simulated event. The results of this paper demonstrate that a reference model like NIEON can be used to benchmark ADS responder performance in response to high-risk initiating behaviors performed by the current driving population.