MOE Key Laboratory for Urban Transportation Complex System Theory and Technology, School of Traffic and Transportation, Beijing Jiaotong University, Beijing, 100044, PR China.
Institute for Transport Studies, University of Leeds, Leeds, LS2 9JT, United Kingdom.
Accid Anal Prev. 2018 Sep;118:114-124. doi: 10.1016/j.aap.2018.06.006. Epub 2018 Jun 18.
Previous studies have shown the effect of a lead vehicle's speed, deceleration rate and headway distance on drivers' brake response times. However, how drivers perceive this information and use it to determine when to apply braking is still not quite clear. To better understand the underlying mechanisms, a driving simulator experiment was performed where each participant experienced nine deceleration scenarios. Previously reported effects of the lead vehicle's speed, deceleration rate and headway distance on brake response time were firstly verified in this paper, using a multilevel model. Then, as an alternative to measures of speed, deceleration rate and distance, two visual looming-based metrics (angular expansion rate θ˙ of the lead vehicle on the driver's retina, and inverse tau τ, the ratio between θ˙ and the optical size θ), considered to be more in line with typical human psycho-perceptual responses, were adopted to quantify situation urgency. These metrics were used in two previously proposed mechanistic models predicting brake onset: either when looming surpasses a threshold, or when the accumulated evidence (looming and other cues) reaches a threshold. Results showed that the looming threshold model did not capture the distribution of brake response time. However, regardless of looming metric, the accumulator models fitted the distribution of brake response times better than the pure threshold models. Accumulator models, including brake lights, provided a better model fit than looming-only versions. For all versions of the mechanistic models, models using τ as the measure of looming fitted better than those using θ˙, indicating that the visual cues drivers used during rear-end collision avoidance may be more close to τ.
先前的研究表明，领车速度、减速度和车间距对驾驶员制动反应时间有影响。然而，驾驶员如何感知这些信息并利用这些信息来确定何时进行制动，这一点仍不是很清楚。为了更好地理解其中的机制，进行了一项驾驶模拟器实验，每个参与者都经历了九种减速场景。本文首先使用多层模型验证了先前报道的领车速度、减速度和车间距对制动反应时间的影响。然后，作为对速度、减速度和距离的替代度量，采用了两种基于视觉逼近的度量（驾驶员视网膜上领车的角扩张率θ˙和倒数τ，即θ˙与光学尺寸θ的比值），这两种度量被认为更符合典型的人类心理感知反应，用于量化情况的紧迫性。这些度量被用于两个之前提出的预测制动起始的机械模型中：一个是当逼近超过阈值，另一个是当积累的证据（逼近和其他线索）达到阈值。结果表明，逼近阈值模型不能捕捉制动反应时间的分布。然而，无论采用哪种逼近度量，积累器模型比纯粹的阈值模型更能拟合制动反应时间的分布。包括制动灯在内的积累器模型比仅采用逼近的版本提供了更好的模型拟合度。对于所有机械模型版本，使用τ作为逼近度量的模型比使用θ˙的模型拟合得更好，这表明驾驶员在避免追尾碰撞时使用的视觉线索可能更接近τ。
