Seacrist Thomas, Maheshwari Jalaj, Sarfare Shreyas, Chingas Gregory, Thirkill Maya, Loeb Helen S
Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.
Drexel University, Philadelphia, Pennsylvania.
Traffic Inj Prev. 2021;22(sup1):S68-S73. doi: 10.1080/15389588.2021.1979529. Epub 2021 Oct 18.
Motor vehicle crashes remain a significant problem. Advanced driver assistance systems (ADAS) have the potential to reduce crash incidence and severity, but their optimization requires a comprehensive understanding of driver-specific errors and environmental hazards in real-world crash scenarios. Therefore, the objectives of this study were to quantify contributing factors using the Strategic Highway Research Program 2 (SHRP 2) Naturalistic Driving Study (NDS), identify potential ADAS interventions, and make suggestions to optimize ADAS for real-world crash scenarios.
A subset of the SHRP 2 NDS consisting of at-fault crashes ( = 369) among teens (16-19 yrs), young adults (20-24 yrs), adults (35-54 yrs) and older adults (70+ yrs) were reviewed to identify contributing factors and potential ADAS interventions. Contributing factors were classified according to National Motor Vehicle Crash Causation Survey pre-crash assessment variable elements. A single critical factor was selected among the contributing factors for each crash. Case reviews with a multidisciplinary panel of industry experts were conducted to develop suggestions for ADAS optimization. Critical factors were compared across at-risk driving groups, gender, and incident type using chi-square statistics and multinomial logistic regression.
Driver error was the critical factor in 94% of crashes. Recognition error (56%), including internal distraction and inadequate surveillance, was the most common driver error sub-type. Teens and young adults exhibited greater decision errors compared to older adults ( < 0.01). Older adults exhibited greater performance errors ( < 0.05) compared to teens and young adults. Automatic emergency braking (AEB) had the greatest potential to mitigate crashes (48%), followed by vehicle-to-vehicle communication (38%) and driver monitoring (24%). ADAS suggestions for optimization included (1) implementing adaptive forward collision warning, AEB, high-speed warning, and curve-speed warning to account for road surface conditions (2) ensuring detection of nonstandard road objects, (3) vehicle-to-vehicle communication alerting drivers to cross-traffic, (4) vehicle-to-infrastructure communication alerting drivers to the presence of pedestrians in crosswalks, and (5) optimizing lane keeping assist for end-departures and pedal confusion.
These data provide stakeholders with a comprehensive understanding of critical factors among at-risk drivers as well as suggestions for ADAS improvements based on naturalistic data. Such data can be used to optimize ADAS for driver-specific errors and help develop more robust vehicle test procedures.
机动车碰撞事故仍然是一个重大问题。先进的驾驶员辅助系统（ADAS）有潜力降低碰撞事故的发生率和严重程度，但其优化需要全面了解现实世界碰撞场景中特定于驾驶员的错误和环境危害。因此，本研究的目的是使用战略公路研究计划2（SHRP 2）自然驾驶研究（NDS）来量化促成因素，识别潜在的ADAS干预措施，并就针对现实世界碰撞场景优化ADAS提出建议。
对SHRP 2 NDS的一个子集进行审查，该子集包括青少年（16 - 19岁）、年轻成年人（20 - 24岁）、成年人（35 - 54岁）和老年人（70岁及以上）中的有责碰撞事故（n = 369），以识别促成因素和潜在的ADAS干预措施。促成因素根据国家机动车碰撞因果调查碰撞前评估变量要素进行分类。为每次碰撞在促成因素中选择一个关键因素。与多学科行业专家小组进行案例审查，以制定ADAS优化建议。使用卡方统计和多项逻辑回归对高危驾驶群体、性别和事故类型的关键因素进行比较。
在94%的碰撞事故中，驾驶员失误是关键因素。识别失误（56%），包括内部分心和监测不足，是最常见的驾驶员失误子类型。与老年人相比，青少年和年轻成年人表现出更大的决策失误（P < 0.01）。与青少年和年轻成年人相比，老年人表现出更大的操作失误（P < 0.05）。自动紧急制动（AEB）减轻碰撞的潜力最大（48%），其次是车对车通信（38%）和驾驶员监测（24%）。ADAS优化建议包括：（1）实施自适应前碰撞预警、AEB、高速预警和弯道速度预警，以考虑路面状况；（2）确保检测到非标准道路物体；（3）车对车通信提醒驾驶员注意交叉交通；（4）车对基础设施通信提醒驾驶员人行横道上有行人；（5）针对驶出末端和踏板混淆优化车道保持辅助。
这些数据为利益相关者提供了对高危驾驶员关键因素的全面理解，以及基于自然驾驶数据的ADAS改进建议。这些数据可用于针对特定于驾驶员的错误优化ADAS，并有助于制定更稳健的车辆测试程序。
