Objective

The paper presents a systematic analysis of drivers’ crash avoidance response during crashes and near-crashes and developed a machine learning-based predictive model that can determine driver maneuver using pre-incident driver behavior and driving context.

Methods

We analyzed 286 naturalistic rear-end crashes and near-crashes from the SHRP2 naturalistic driving study. All the events were manually reduced using face video (face and forward) and kinematic responses. In this paper, we developed new reduction variables that enhanced the understanding of drivers’ gaze behavior and roadway attention behavior during these events. These features reflected how the event criticality, measured using time to collision, related to drivers’ pre-incident behavior (secondary behavior, gaze behavior), and drivers’ perception of the event (physical reaction and maneuver). The imperative understanding of such relations was validated using a random forest- (RF) based classifier, which efficiently predicted if a driver was going to brake or change the lane as an avoidance maneuver.

Results

The RF presented in this paper effectively explored the nonlinear patterns in the data and was highly accurate (∼96 %) in its prediction. A further analysis of the RF model showed that six features played a pivotal role in the decision logic. These included the drivers’ last glance duration before the event, last glance eccentricity, duration of ‘eyes on road’ immediately before the event, the time instance and criticality when the driver perceives the threat as well as acknowledge the threat, and possibility of an escape path in the adjacent lane. Using partial dependency plots, we also showed how different thresholds of these feature variables determined the drivers’ maneuver intention.

Conclusions

In this paper we analyzed driving context, drivers’ behavior, event criticality, and drivers’ response in a unified structure to predict their avoidance response. To the best of our knowledge, this is the first such effort where large-scale naturalistic data (crashes and near crashes) was analyzed for prediction of drivers’ maneuver and determined key behavioral and contextual factors that contribute to this avoidance maneuver.

中文翻译：

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SHRP2后端崩溃和近崩溃中的转向或制动回避响应：决策树方法

客观的

本文对撞车和接近撞车时驾驶员的避撞反应进行了系统分析，并开发了一种基于机器学习的预测模型，该模型可以使用事前驾驶员的行为和驾驶环境来确定驾驶员的操作方式。

方法

我们从SHRP2自然驾驶研究中分析了286个自然主义的后端碰撞和近碰撞。使用面部视频（面部和向前）和运动学响应手动减少了所有事件。在本文中，我们开发了新的减少变量，可增强对这些事件期间驾驶员的凝视行为和道路注意行为的了解。这些特征反映了使用碰撞时间衡量的事件临界度与驾驶员的事前行为（次要行为，凝视行为）以及驾驶员对事件的感知（身体反应和操纵）之间的关系。使用基于随机森林（RF）的分类器验证了对这种关系的当务之急，该分类器有效地预测了驾驶员是否要刹车或改变车道，从而避免了回旋行为。

结果

本文介绍的RF有效地探索了数据中的非线性模式，并且其预测具有很高的准确度（约96％）。对RF模型的进一步分析表明，六个功能在决策逻辑中起着关键作用。其中包括驾驶员在事件发生前的最后一眼持续时间，最后一眼的偏心率，事件发生前的“道路上的目光”持续时间，驾驶员感知威胁并确认威胁的时间实例和严重程度，以及发生事故的可能性。相邻车道的逃生路径。通过使用局部相关性图，我们还展示了这些特征变量的不同阈值如何确定驾驶员的操纵意图。

结论

在本文中，我们以统一的结构分析了驾驶环境，驾驶者的行为，事件关键性和驾驶者的反应，以预测他们的回避反应。据我们所知，这是第一次这样的尝试，其中分析了大型自然数据（崩溃和接近碰撞）以预测驾驶员的操作并确定了有助于避免操作的关键行为和背景因素。