A Crash Injury Model Involving Autonomous Vehicle: Investigating of Crash and Disengagement Reports

Abstract

Autonomous vehicles (AVs) are being extensively tested on public roads in several states in the USA, such as California, Florida, Nevada, and Texas. AV utilization is expected to increase into the future, given rapid advancement and development in sensing and navigation technologies. This will eventually lead to a decline in human driving. AVs are generally believed to mitigate crash frequency, although the repercussion of AVs on crash severity is ambiguous. For the data-driven and transparent deployment of AVs in California, the California Department of Motor Vehicles (CA DMV) commissioned AV manufacturers to draft and publish reports on disengagements and crashes. This study performed a comprehensive assessment of CA DMV data from 2014 to 2019 from a safety standpoint, and some trends were discerned. The results show that decrement in automated disengagements does not necessarily imply an improvement in AV technology. Contributing factors to the crash severity of an AV are not clearly defined. To further understand crash severity in AVs, the features and issues with data are identified and discussed using different machine learning techniques. The CA DMV accident report data were utilized to develop a variety of crash AV severity models focusing on the injury for all crash typologies. Performance metrics were discussed, and the bagging classifier model exhibited the best performance among different candidate models. Additionally, the study identified potential issues with the CA DMV data reporting protocol, which is imperative to share with the research community. Recommendations are provided to enhance the existing reports and append new domains.

Crash and disengagement data of autonomous vehicles on public roads in California

“…The disengagement trends with appended data presented in our recently published study 14 are coherent with a previous study 4 . The crash typology distribution and imbalance in injury outcome in our study 14 are also coherent with another previous study 11 . These validation methods are discussed in detail below.…”

“…Additionally, previously published studies were replicated with our processed crash and disengagement data, and the results were found to be consistent. The replicated results can be found in a recent study published by the authors 14 . The disengagement trends with appended data presented in our recently published study 14 are coherent with a previous study 4 .…”

“…The replicated results can be found in a recent study published by the authors 14 . The disengagement trends with appended data presented in our recently published study 14 are coherent with a previous study 4 . The crash typology distribution and imbalance in injury outcome in our study 14 are also coherent with another previous study 11 .…”

“…Another study 11 which analysed data until the year 2018, reported 63% of crashes as rear-ended. A more recently published study 14 that used our dataset reported 61.78% (160 out of 259) rear-ended crashes. Consistent numbers reported corroborates validation of our dataset, as shown in Fig.…”

“…The authors pointed out the necessity of more advanced and robust collision narrative reporting for better analysis of the critical events. More recently, machine learning techniques were used to evaluate the crash severity of AVs 14 .…”