Safety critical event prediction through unified analysis of driver and vehicle volatilities: Application of deep learning methods
Safety critical event prediction through unified analysis of driver and vehicle volatilities: Application of deep learning methods
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DOI：
10.1016/j.aap.2020.105949
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发表时间：
2021-03-01
影响因子：
5.9
通讯作者：
Qi, Hairong
中科院分区：
文献类型：
--
作者：
Arvin, Ramin;Khattak, Asad J.;Qi, Hairong
交通安全与驾驶行为高度相关，尤其是人为错误在很大一部分事故中起着关键作用。现代仪器和计算资源允许对驾驶员、车辆和道路/环境进行监控，以从多维数据流中提取碰撞的主要指标。为了量化驾驶员行为和车辆运动学中超出正常范围的变化，应用了波动性的概念。该研究使用自然驾驶数据测量驾驶员-车辆波动。通过整合和融合多个实时数据流，即，驾驶员分心、车辆运动和运动学以及驾驶不稳定性，本研究旨在预测安全关键事件的发生，并向驾驶员和周围车辆提供适当的反馈。使用包含7566个正常驾驶事件和1315个严重事件（即，碰撞和接近碰撞）、车辆运动学和从3500多名驾驶员收集的驾驶员行为。为了捕捉时间序列数据中的局部依赖性和波动性，应用了1D-CNN、长短期记忆（LSTM）和1DCNN-LSTM。车辆运动学、驾驶波动性和受损驾驶（在分心方面）被用作输入参数。结果显示，1DCNN-LSTM模型提供了最佳性能，准确率为95.45%，预测73.4%的碰撞，准确率为95.67%。使用CNN层提取额外的特征，并解决观察之间的时间依赖性，这有助于网络学习驾驶模式和波动行为。该模型可用于实时监控驾驶行为，并向低级别自动驾驶汽车的驾驶员提供警告和警报，降低其碰撞风险。
Transportation safety is highly correlated with driving behavior, especially human error playing a key role in a large portion of crashes. Modern instrumentation and computational resources allow for the monitorization of driver, vehicle, and roadway/environment to extract leading indicators of crashes from multi-dimensional data streams. To quantify variations that are beyond normal in driver behavior and vehicle kinematics, the concept of volatility is applied. The study measures driver-vehicle volatilities using the naturalistic driving data. By integrating and fusing multiple real-time streams of data, i.e., driver distraction, vehicular movements and kinematics, and instability in driving, this study aims to predict occurrence of safety critical events and generate appropriate feedback to drivers and surrounding vehicles. The naturalistic driving data is used which contains 7566 normal driving events, and 1315 severe events (i.e., crash and near-crash), vehicle kinematics, and driver behavior collected from more than 3500 drivers. In order to capture the local dependency and volatility in time-series data 1D-Convolutional Neural Network (1D-CNN), Long Short-Term Memory (LSTM), and 1DCNN-LSTM are applied. Vehicle kinematics, driving volatility, and impaired driving (in terms of distraction) are used as the input parameters. The results reveal that the 1DCNN-LSTM model provides the best performance, with 95.45% accuracy and prediction of 73.4% of crashes with a precision of 95.67%. Additional features are extracted with the CNN layers and temporal dependency between observations is addressed, which helps the network learn driving patterns and volatile behavior. The model can be used to monitor driving behavior in real-time and provide warnings and alerts to drivers in low-level automated vehicles, reducing their crash risk.