School of Transportation and Logistics, East China Jiaotong University, Nanchang 330013, Jiangxi, China.
Tsinghua University Suzhou Automotive Research Institute, Suzhou 215134, Jiangsu, China.
Comput Intell Neurosci. 2022 Jan 28;2022:5698393. doi: 10.1155/2022/5698393. eCollection 2022.
"Road rage," namely, driving anger, has been becoming increasingly common in auto era. As "road rage" has serious negative impact on road safety, it has attracted great concern to relevant scholar, practitioner, and governor. This study aims to propose a model to effectively and efficiently detect driving anger states with different intensities for taking targeted intervening measures in intelligent connected vehicles. Forty-two private car drivers were enrolled to conduct naturalistic experiments on a predetermined and busy route in Wuhan, China, where drivers' anger can be induced by various incentive events like weaving/cutting in line, jaywalking, and traffic congestion. Then, a data-driven model based on double-layered belief rule base is proposed according to the accumulation of the naturalistic experiments data. The proposed model can be used to effectively detect different driving anger states as a function of driver characteristics, vehicle motion, and driving environments. The study results indicate that average accuracy of the proposed model is 82.52% for all four-intensity driving anger states (none, low, medium, and high), which is 1.15%, 1.52%, 3.53%, 5.75%, and 7.42%, higher than C4.5, BPNN, NBC, SVM, and kNN, respectively. Moreover, the runtime ratio of the proposed model is superior to that of those models except for C4.5. Hence, the proposed model can be implemented in connected intelligent vehicle for detecting driving anger states in real time.
“路怒症”，即驾驶愤怒，在汽车时代变得越来越普遍。由于“路怒症”对道路安全有严重的负面影响，它引起了相关学者、从业者和政府的极大关注。本研究旨在提出一个模型，以有效地检测不同强度的驾驶愤怒状态，以便在智能互联车辆中采取有针对性的干预措施。
四十二名私家车驾驶员在中国武汉的一条预定繁忙路线上进行了自然主义实验，在那里，驾驶员的愤怒可以通过各种激励事件（如穿插、乱穿马路和交通拥堵）引发。然后，根据自然主义实验数据的积累，提出了一种基于双层置信规则库的数据驱动模型。
所提出的模型可以有效地检测不同的驾驶愤怒状态，作为驾驶员特征、车辆运动和驾驶环境的函数。研究结果表明，所提出的模型对所有四个强度的驾驶愤怒状态（无、低、中、高）的平均准确率为 82.52%，分别比 C4.5、BPNN、NBC、SVM 和 kNN 高出 1.15%、1.52%、3.53%、5.75%和 7.42%。此外，所提出模型的运行时间比除 C4.5 之外的其他模型都要优越。因此，所提出的模型可以在联网智能车辆中实时检测驾驶愤怒状态。
