Examining accident reports involving autonomous vehicles in California

Abstract

Autonomous Vehicle technology is quickly expanding its market and has found in Silicon Valley, California, a strong foothold for preliminary testing on public roads. In an effort to promote safety and transparency to consumers, the California Department of Motor Vehicles has mandated that reports of accidents involving autonomous vehicles be drafted and made available to the public. The present work shows an in-depth analysis of the accident reports filed by different manufacturers that are testing autonomous vehicles in California (testing data from September 2014 to March 2017). The data provides important information on autonomous vehicles accidents’ dynamics, related to the most frequent types of collisions and impacts, accident frequencies, and other contributing factors. The study also explores important implications related to future testing and validation of semi-autonomous vehicles, tracing the investigation back to current literature as well as to the current regulatory panorama.

Assessing the Safety and Reliability of Autonomous Vehicles from Road Testing

“…A previous statistical study [36] found that some key AV reliability measures, e.g. pcm for AVs, appear constant over time but this is not enough to support making it a modelling assumption.…”

Studying the Safety Impact of Autonomous Vehicles Using Simulation-Based Surrogate Safety Measures

“…Rau et al [17] developed a method to identify crashes, which could be addressed by AV technology, by mapping automated vehicle functions to five layers of crash information (location, precrash scenario, driving conditions, travel speed, and driver condition). Real-world data from AV testing in California has increasingly been utilised in recent research [16,[18][19][20]. For example, Schoettle and Sivak [18] found that AVs were not at fault in any crashes and the overall injury severity was lower for crashes involving AVs than for crashes involving human-driven vehicles (HVs).…”

A bi‐level model to optimize road networks for a mixture of manual and automated driving: An evolutionary local search algorithm

“…Motorways usually have highest standards and can be suitable for AD with minimal adjustments. Freeways and urban arterials can have high standards as well but they might include segments with unsegregated traffic and complex intersections that require adjustments given that 89% of the reported accidents involving an AV have happened at an intersection (Favarò et al., ). Since we are not aware of any study that provides accurate cost estimates for the adjustments mentioned in the introduction, we assumed the following costs and provide a sensitivity analysis to show how variations in the adjustment costs can affect the results: 100,000 €/km for local roads, 30,000 for freeways, and 5,000 for motorways.…”

“…These links include all roads with flow (mobility) function and some roads with distribution function. They correspond to motorways, expressways, and main urban roads which have been shown to be the safest roads for AVs and include none or very few intersections, which are where most AV accidents and disengagements have happened (Favarò et al., ; Favarò et al., ).…”

“…Moreover, number of disengagements differ significantly based on road type (lower in motorways and higher in urban streets). According to Favarò, Nader, Eurich, Tripp, and Varadaraju (), 89% of the reported accidents involving an AV happened at an intersection, with 32% occurring in city roads. Finally, it is stated in Favarò, Eurich, and Nader () that more than 10% of disengagements are attributed to external factors with infrastructure and other road users being the main categories.…”