Waymo simulated driving behavior in reconstructed fatal crashes within an autonomous vehicle operating domain. (December 2021)

• Record Type: Journal Article
• Authors: Scanlon, John M.; Kusano, Kristofer D.; Daniel, Tom; Alderson, Christopher; Ogle, Alexander; Victor, Trent
• Is Part Of: Accident analysis and prevention. Volume 163(2021)
• Publication Date: 2021-12
• Subjects: Safety impact -- Safety benefits -- Automated driving systems -- Counterfactual simulation; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2021.106454
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: A novel alignment methodology was employed for counterfactual simulation of reconstructed collisions. The simulated Waymo Driver prevented the initiation of every fatal collision in the dataset without performing urgent evasive maneuvers. As a responder, the system was estimated to prevent 82% of fatal collisions and mitigate an additional 10%. The majority (63%) of the avoided responder scenarios were prevented without the need for urgent evasive action. Abstract: Preventing and mitigating high severity collisions is one of the main opportunities for Automated Driving Systems (ADS) to improve road safety. This study evaluated the Waymo Driver's performance within real-world fatal collision scenarios that occurred in a specific operational design domain (ODD). To address the rare nature of high-severity collisions, this paper describes the addition of novel techniques to established safety impact assessment methodologies. A census of fatal, human-involved collisions was examined for years 2008 through 2017 for Chandler, AZ, which overlaps the current geographic ODD of the Waymo One fully automated ride-hailing service. Crash reconstructions were performed on all available fatal collisions that involved a passenger vehicle as one of the first collision partners and an available map in this ODD to determine the pre-impact kinematics of the vehicles involved in the original crashes. The final dataset consisted of a total of 72 crashes and 91 vehicle actors (52 initiators and 39 responders) for simulations. Next, a novel counterfactual "what-if'' simulation method was developed to synthetically replace human-driven crash participants one at a time with the Waymo Driver. This study focused on the Waymo Driver's performance when replacing one of the first two collision partners. The results of these simulations showed that the Waymo Driver was successful in avoiding all collisions when replacing the crash initiator, that is, the road user who made the initial, unexpected maneuver leading to a collision. Replacing the driver reacting (the responder) to the actions of the crash initiator with the Waymo Driver resulted in an estimated 82% of simulations where a collision was prevented and an additional 10% of simulations where the collision severity was mitigated (reduction in crash-level serious injury risk). The remaining 8% of simulations with the Waymo Driver in the responder role had a similar outcome to the original collision. All of these "unchanged" collisions involved both the original vehicle and the Waymo Driver being struck in the rear in a front-to-rear configuration. These results demonstrate the potential of fully automated driving systems to improve traffic safety compared to the performance of the humans originally involved in the collisions. The findings also highlight the major importance of driving behaviors that prevent entering a conflict situation (e.g. maintaining safe time gaps and not surprising other road users). However, methodological challenges in performing single instance counterfactual simulations based solely on police report data and uncertainty in ADS performance may result in variable performance, requiring additional analysis and supplemental methodologies. This study's methods provide insights on rare, severe events that would otherwise only be experienced after operating in extreme real-world driving distances (many billions of driving miles).