Safety in higher level automated vehicles: Investigating edge cases in crashes of vehicles equipped with automated driving systems

Nastaran Moradloo; Iman Mahdinia; Asad J Khattak

doi:10.1016/j.aap.2024.107607

Safety in higher level automated vehicles: Investigating edge cases in crashes of vehicles equipped with automated driving systems

Accid Anal Prev. 2024 May 8:203:107607. doi: 10.1016/j.aap.2024.107607. Online ahead of print.

Authors

Nastaran Moradloo¹, Iman Mahdinia², Asad J Khattak³

Affiliations

¹ Department of Civil & Environmental Engineering, The University of Tennessee, TN 37996, USA. Electronic address: nmoradlo@vols.utk.edu.
² Safe Transportation Research & Education Center, The University of California Berkeley, CA 94704, USA. Electronic address: imahdinia@berkeley.edu.
³ Department of Civil & Environmental Engineering, The University of Tennessee, TN 37996, USA. Electronic address: akhattak@utk.edu.

PMID: 38723333
DOI: 10.1016/j.aap.2024.107607

Abstract

With emerging Automated Driving Systems (ADS) representing Automated Vehicles (AVs) of Level 3 or higher as classified by the Society of Automotive Engineers, several AV manufacturers are testing their vehicles on public roadways in the U.S. The safety performance of AVs has become a major concern for the transportation industry. Several ADS-equipped vehicle crashes have been reported to the National Highway Traffic Safety Administration (NHTSA) in recent years. Scrutinizing these crashes can reveal rare or complex scenarios beyond the normal capabilities of AV technologies called "edge cases." Investigating edge-case crashes helps AV companies prepare vehicles to handle these unusual scenarios and, as such, improves traffic safety. Through analyzing the NHTSA data from July 2021 to February 2023, this study utilizes an unsupervised machine learning technique, hierarchical clustering, to identify edge cases in ADS-equipped vehicle crashes. Fifteen out of 189 observations are identified as edge cases, representing 8 % of the population. Injuries occurred in 10 % of all crashes (19 out of 189), but the proportion rose to 27 % for edge cases (4 out of 15 edge cases). Based on the results, edge cases could be initiated by AVs, humans, infrastructure/environment, or their combination. Humans can be identified as one of the contributors to the onset of edge-case crashes in 60 % of the edge cases (9 out of 15 edge cases). The main scenarios for edge cases include unlawful behaviors of crash partners, absence of a safety driver within the AV, precrash disengagement, and complex events challenging for ADS, e.g., unexpected obstacles, unclear road markings, and sudden and unexpected changes in traffic flow, such as abrupt road congestion or sudden stopped traffic from a crash. Identifying and investigating edge cases is crucial for improving transportation safety and building public trust in AVs.

Keywords: Automated driving systems; Automated vehicle crash; Edge case; Hierarchical clustering; NHTSA data; Unsupervised machine learning.