The role of pre-crash driving instability in contributing to crash intensity using naturalistic driving data
The role of pre-crash driving instability in contributing to crash intensity using naturalistic driving data
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DOI：
10.1016/j.aap.2019.07.002
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发表时间：
2019-11-01
影响因子：
5.9
通讯作者：
Khattak, Asad J.
中科院分区：
文献类型：
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作者：
Arvin, Ramin;Kamrani, Mohsen;Khattak, Asad J.
虽然仅在美国，每年的撞车成本就超过了10亿美元，但高分辨率自然驾驶数据的可用性为研究人员提供了一个机会，可以对撞车的影响因素进行深入分析，并设计适当的干预措施。虽然警方报告的碰撞数据提供了有关碰撞的信息，但本研究利用了SHRP 2自然驾驶研究（NDS），这是一个独特的数据集，除了行程和车辆性能特征外，还提供了有关正常，碰撞前和接近碰撞情况下驾驶员行为的详细信息。本文研究了碰撞前驾驶不稳定性或驾驶波动性在碰撞强度中的作用（通过分析微观车辆运动学数据，在从轮胎撞击到受伤碰撞的4点尺度上测量）。NDS数据不仅用于研究车辆在空间中的运动，而且还用于研究碰撞前车辆的不稳定性及其对碰撞强度的影响。分析了包含617起碰撞事件的数据子集，其中约有18万条时间轨迹。为了量化驾驶不稳定性，分析碰撞前车辆运动的微观变化或波动。具体而言，九个措施的前碰撞驾驶波动性计算和用于解释碰撞强度。虽然大多数的措施是显着相关的碰撞强度，大量的正相关性观察到两个措施，代表速度和减速波动。固定参数和随机参数概率单位模型的建模结果表明，波动性是增加严重崩溃概率的主要因素之一。此外，正如预期的那样，撞车前的速度与强度结果高度相关。有趣的是，分心和攻击性驾驶与驾驶波动性高度相关，并对碰撞强度产生重大间接影响。由于不稳定的驾驶作为碰撞强度的领先指标，考虑到本研究中分析的碰撞，当观察到不稳定的行为时，对主体车辆驾驶员和附近车辆的早期警告和警报可能会有所帮助。
While the cost of crashes exceeds $1 Trillion a year in the U.S. alone, the availability of high-resolution naturalistic driving data provides an opportunity for researchers to conduct an in-depth analysis of crash contributing factors, and design appropriate interventions. Although police-reported crash data provides information on crashes, this study takes advantage of the SHRP2 Naturalistic Driving Study (NDS) which is a unique dataset that allows new insights due to detailed information on driver behavior in normal, pre-crash, and near-crash situations, in addition to trip and vehicle performance characteristics. This paper investigates the role of pre-crash driving instability, or driving volatility, in crash intensity (measured on a 4-point scale from a tire-strike to an injury crash) by analyzing microscopic vehicle kinematic data. NDS data are used to investigate not only the vehicle movements in space but also the instability of vehicles prior to the crash and their contribution to crash intensity using path analysis. A subset of the data containing 617 crash events with around 0.18 million temporal trajectories are analyzed. To quantify driving instability, microscopic variations or volatility in vehicular movements before a crash are analyzed. Specifically, nine measures of pre-crash driving volatility are calculated and used to explain crash intensity. While most of the measures are significantly correlated with crash intensity, substantial positive correlations are observed for two measures representing speed and deceleration volatilities. Modeling results of the fixed and random parameter probit models revealed that volatility is one of the leading factors increasing the probability of a severe crash. Additionally, the speed prior to a crash is highly correlated with intensity outcomes, as expected. Interestingly, distracted and aggressive driving are highly correlated with driving volatility and have substantial indirect effects on crash intensity. With volatile driving serving as a leading indicator of crash intensity, given the crashes analyzed in this study, early warnings and alerts for the subject vehicle driver and proximate vehicles can be helpful when volatile behavior is observed.