In-depth analysis of crash contributing factors and potential ADAS interventions among at-risk drivers using the SHRP 2 naturalistic driving study
In-depth analysis of crash contributing factors and potential ADAS interventions among at-risk drivers using the SHRP 2 naturalistic driving study
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DOI：
10.1080/15389588.2021.1979529
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发表时间：
2021-09-12
影响因子：
2
通讯作者：
Loeb, Helen S.
中科院分区：
文献类型：
--
作者：
Seacrist, Thomas;Maheshwari, Jalaj;Loeb, Helen S.
目的机动车碰撞事故仍然是一个严重的问题。先进的驾驶员辅助系统(ADA)具有降低事故发生率和严重程度的潜力，但它们的优化需要全面了解真实世界碰撞场景中特定于驾驶员的错误和环境危害。因此，本研究的目标是利用战略公路研究计划2(SHRP 2)的自然主义驾驶研究(NDS)来量化影响因素，确定潜在的ADAS干预措施，并针对现实世界的碰撞情景提出优化ADAS的建议。方法回顾SHRP 2 NDS的一个子集，包括青少年(16-19岁)、年轻人(20-24岁)、成年人(35-54岁)和老年人(70+岁)中的故障撞车(n=369)，以确定导致事故的因素和潜在的ADAS干预措施。根据全国机动车碰撞原因调查、碰撞前评估变量要素对影响因素进行了分类。从导致每一次坠机的因素中选择了一个关键因素。与多学科行业专家小组一起进行了案例审查，以制定针对ADAS优化的建议。使用卡方统计和多项Logistic回归对高危驾驶人群、性别和事故类型的关键因素进行了比较。结果驾驶员失误是造成94%的车祸的关键因素。识别错误(56%)是最常见的驾驶员错误类型，包括内部分心和监控不足。与老年人相比，青少年和年轻人表现出更大的决策错误(p<；0.01)。与青少年和年轻人相比，老年人表现出更大的表现错误(p<；0.05)。自动紧急制动(AEB)对减少碰撞的潜力最大(48%)，其次是车与车之间的通信(38%)和驾驶员监控(24%)。ADAS的优化建议包括(1)实施自适应前向碰撞警告、AEB、高速警告和曲线速度警告，以应对路面状况(2)确保检测到非标准道路对象，(3)车与车之间的通信提醒司机注意交叉交通，(4)车与基础设施之间的通信提醒司机注意人行横道上的行人，以及(5)优化车道保持辅助，以防止尾部偏离和踏板混乱。结论：这些数据为利益相关者提供了对高危驱动因素关键因素的全面了解，并基于自然数据提出了改进ADAS的建议。这些数据可用于优化驾驶员特定错误的ADAS，并帮助开发更强大的车辆测试程序。
Objective 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. Methods A subset of the SHRP 2 NDS consisting of at-fault crashes (n = 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. Results 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 (p < 0.01). Older adults exhibited greater performance errors (p < 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. Conclusions 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.