State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing, China.
School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan, China.
Accid Anal Prev. 2020 Sep;145:105680. doi: 10.1016/j.aap.2020.105680. Epub 2020 Jul 21.
Traffic accident statistics have shown the necessity of risk assessment when driving in the dynamic traffic environment. If the risk associated with different traffic elements (i.e., road, environment and vehicles) could be evaluated accurately, potential accidents could be significantly avoided or mitigated. This paper proposes a driving risk assessment model that can quantitatively evaluate the driving risk associated with intelligent vehicles via the coupled analysis of different traffic elements. First, we present a concept of the internal field and external field for establishing the driving risk coupling model, through employing the internal field to define the risk range of driver's perspective and the external field to calculate the risk coefficients of those traffic elements. Then, the relative risk coefficients are computed by incorporating both naturalistic driving study (NDS) and driver attitude questionnaire (DAQ) using a multinomial logit model. Specifically, we perform a large-scale naturalistic driving study to investigate the objective driving risks. Typical driver behavior parameters, such as velocity, time headway, and acceleration, are analyzed. Besides, a self-reported survey of 364 drivers is conducted to subjectively evaluate the potential risks that drivers may face in various situations. Finally, validation of the model is conducted by comparing the accuracy with the typical risk assessment index, i.e., TTC and THW. Results demonstrate that the proposed approach is effective in evaluating the comprehensive driving risks by quantifying the influence factors of driving risks in dynamic environments.
交通事故统计数据表明，在动态交通环境中驾驶时进行风险评估的必要性。如果能够准确评估与不同交通元素（即道路、环境和车辆）相关的风险，就可以显著避免或减轻潜在事故。本文提出了一种驾驶风险评估模型，通过对不同交通元素的耦合分析，可以定量评估与智能车辆相关的驾驶风险。首先，我们提出了内部场和外部场的概念，以建立驾驶风险耦合模型，通过内部场来定义驾驶员视角的风险范围，通过外部场来计算那些交通元素的风险系数。然后，通过使用多项逻辑回归模型结合自然驾驶研究（NDS）和驾驶员态度问卷（DAQ）来计算相对风险系数。具体来说，我们进行了大规模的自然驾驶研究来调查客观驾驶风险。分析了典型的驾驶员行为参数，如速度、时间间隔和加速度。此外，我们还对 364 名驾驶员进行了一份自我报告调查，以主观评估驾驶员在各种情况下可能面临的潜在风险。最后，通过与典型风险评估指标（即 TTC 和 THW）进行比较来验证模型的准确性。结果表明，该方法通过量化动态环境中驾驶风险的影响因素，有效地评估了综合驾驶风险。
