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

机译:接近自主驾驶与谨慎乐观:道路交通伤害的分析基于现场试验涉及自主车辆数据

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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.Data 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.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).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.
机译:检查的模式及相关因素道路交通伤害(rti)涉及自治车辆(AVs)和讨论的公共卫生自治的影响和挑战开车。交通事故涉及AVs。提交给加州吗汽车制造商与许可AV操作测试在公共道路上行驶。分析和χ2分析或确切概率法进行损伤模式和描述检查受伤的影响因素结果,分别。回归使用瓦尔德测试来计算,或者调整或独联体(AOR)和95%。采用双尾概率(p < 0.05)显示统计学意义。记录24人受伤19崩溃涉及AVs,高估了发病率18.05每100崩溃。受伤AV居住者,取代脆弱道路使用者的主要受害者。最常见的受伤位置。在贫穷的照明是rti (AOR的风险更大6.37, 95%可信区间1.47到27.54)。易受伤害的道路使用者或事件发生上下班期间导致更多的受害者(p < 0.05)。道路交通比传统模式安全(p = 0.468)。改进和commute-period的规定交通和弱势道路使用者加强AV-related道路安全。AVs并未表现出的潜力大大减少rti。AVs是更可取的,多方面的努力,包括立法、聪明的道路,和知识传播活动,是相当需要加快发展接受。

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