Approaching autonomous driving with cautious optimism: analysis of road traffic injuries involving autonomous vehicles based on field test data
Wanbao Ye, Wang Chuanlin, Fuxiang Chen, Shuzhen Yan, Liping Li
Abstract
OBJECTIVES: To 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. METHODS: 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. RESULTS: 133 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). CONCLUSIONS: Poor 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.