Autonomous Vehicle Accident Data Analysis: California OL 316 Reports: 2015–2020

撞车 碰撞 航空学 机动车碰撞 碰撞试验 工程类 法律工程学 计算机安全 运输工程 毒物控制 计算机科学 人为因素与人体工程学 医疗急救 医学 程序设计语言
作者
Roger L. McCarthy
出处
期刊:ASCE-ASME journal of risk and uncertainty in engineering systems, [ASM International]
卷期号:8 (3) 被引量:42
标识
DOI:10.1115/1.4051779
摘要

Abstract Six years (2015–2020) of autonomous vehicle (AV Level 3) crash data from California's (CA) OL 316 collision reports of AV crashes while in the autonomous mode (AM) or disengaged from AM just before the collision, divided by the associated CA AV make and mileage driven in the AM, are compared with the University of Michigan Transportation Research Institute (UMTRI) corrected human driver crash frequency. AV test drivers in CA mandatorily self-report every crash, whereas average drivers underreport minor accidents, so the UMTRI reporting correction factor permits comparison. CA's AV AM mileage is only a few million miles over the last few years, with virtually no police-reported crash data yet available. OL 316 crash consequence data (e.g., damage, injuries, etc.) is anecdotal and inconsistently self-reported. The CA collision report data indicate the CA AV test fleet exhibits multiples of the human crash frequency. Invariably, the AV accidents are the human driver's fault, with a majority being rear collisions. The human drivers appear less able to anticipate the AV's more conservative driving. CA's AV experience predicts more widespread deployment of existing AV technologies is not likely to reduce vehicle crash frequency, at least in the short term, and might well increase it.
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