Approaching autonomous driving with cautious optimism: analysis of road traffic injuries involving autonomous vehicles based on field test data

Abstract

ObjectivesTo examine the patterns and associated factors of road traffic injuries (RTIs) involving autonomous vehicles (AVs) and to discuss the public health implications and challenges of autonomous driving.MethodsData were extracted from the reports of traffic crashes involving AVs. All the reports were submitted to the California Department of Motor Vehicles by manufacturers with permission to operate AV test on public roads. Descriptive analysis and χ2 analysis or Fisher’s exact test was conducted to describe the injury patterns and to examine the influencing factors of injury outcomes, respectively. Binary logistic regression using the Wald test was employed to calculate the OR, adjusted OR (AOR) and 95% CIs. A two-tailed probability (p<0.05) was adopted to indicate statistical significance.Results133 reports documented 24 individuals injured in 19 crashes involving AVs, with the overestimated incidence rate of 18.05 per 100 crashes. 70.83% of the injured were AV occupants, replacing vulnerable road users as the leading victims. Head and neck were the most commonly injured locations. Driving in poor lighting was at greater risk of RTIs (AOR 6.37, 95% CI 1.47 to 27.54). Collisions with vulnerable road users or incidents happening during commute periods led to a greater number of victims (p<0.05). Autonomous mode cannot perform better than conventional mode in road traffic safety to date (p=0.468).ConclusionsPoor lighting improvement and the regulation of commute-period traffic and vulnerable road users should be strengthened for AV-related road safety. So far AVs have not demonstrated the potential to dramatically reduce RTIs. Cautious optimism about AVs is more advisable, and multifaceted efforts, including legislation, smarter roads, and knowledge dissemination campaigns, are fairly required to accelerate the development and acceptance.

Investigating the impacts of autonomous vehicles on crash severity and traffic safety

“…It was determined through an analysis of CA DMV collision reports that most AV crashes result in minor damage to both the AV and the other vehicle, and most of the personal injuries involve back pain (Dixit et al, 2016). Back, head and neck injuries are the most common type of injury, with AV occupants representing 70.83% of those injured (Ye et al, 2021). As safety is considered the primary advantage of AVs, the factors Frontiers in Built Environment frontiersin.org associated with increased crash severity were identified and are depicted in Table 2.…”

“…The Society of Automotive Engineers (SAE) outlines six levels of automation: Level 0 is completely manual, Levels 1 to 3 are partially autonomous, and Levels 4 to 5 are completely autonomous (Favarò et al, 2018). As the level of automation rises, the vehicle requires less human involvement and support to function safely, which minimizes driver-related road traffic hazards (Ye et al, 2021). Regardless of their level of automation, however, safety remains a concern, and extensive testing is performed to ensure that they do in fact drastically reduce the number of accidents.…”

Investigating safety and liability of autonomous vehicles: Bayesian random parameter ordered probit model analysis

“…An in-depth investigation revealed that perception-reaction time, inaccurate identification and insufficient path planning were significant causes of AV crashes. Similarly, Song et al (2021) displayed that the most representative pattern in AV crashes was “collision following AV stop”, and Ye et al (2021) estimated traffic injuries involving AVs, which showed that autonomous mode can’t perform better in road traffic safety. From the perspective of vulnerable road users (VRUs), Kutela et al (2022) explored patterns of AV crashes and it was found that crosswalks, intersections, traffic signals and movements of AVs were critical for VRUs-AV related crashes.…”

How PTV Vissim Has Been Calibrated for the Simulation of Automated Vehicles in Literature?

“…Automated driving systems are often referred to as "autonomous" (e.g., [4][5][6]) and "automated" (e.g., [1,7,8]).…”

“…ere are different methods used in the literature to assess the impacts of AVs including field test experiments [4,21] and simulation [10]. Microscopic simulation is one of the most popular methods in AVs' impact assessment on traffic flow in the literature.…”