Abstract
“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.
Highlights
“Road rage” is a particular driving emotion resulting from pressure or frustration from daily life or adverse traffic environments or discourteous behaviors from surrounding traffic participants [1]. e driving emotion has been becoming an increasingly common issue affecting road safety in auto era all over the world
When depicting the cut-off point in a coordinate system with horizontal and vertical ordinate, represented by True Positive Rate (TPR) and false positive rate (FPR), respectively, a complete receiver operating characteristic (ROC) curve will be formed by connecting all the possible cut-off points with a broken line in the coordinate system
Every participant inevitably came across sorts of incentive situations like jaywalking, motorcycle occupying road, weaving or cutting in line, traffic jam or congestion, and traffic light waiting, especially in morning or evening rush hours. e study results indicate that the hit rate of anger under the incentive situations reaches 74.25%, significantly higher than that under no incentive situations, and the highest hit rate (82.03%) of anger happened under the stimulus of jaywalking/motorcycle occupying road
Summary
“Road rage” is a particular driving emotion resulting from pressure or frustration from daily life or adverse traffic environments or discourteous behaviors from surrounding traffic participants [1]. e driving emotion has been becoming an increasingly common issue affecting road safety in auto era all over the world. E driving emotion has been becoming an increasingly common issue affecting road safety in auto era all over the world. A report from National Highway Safety Administration of US indicated that the ratio of traffic accidents because of emotional driving like road rage accounted for 9.2%∼14.8% of the total [2]. In. China, another report showed that road rage brought about 17.33 million illegal acts, leading to 83,100 traffic accidents in 2015, 1.22% higher than that of 2014 [3]. As anger has a negative impact on a driver’s perception, identification, decision, and volition process, the driver will inevitably have a degraded driving performance [4]. Erefore, a driving anger detection/warning method should be designed for effective intervening to deal with road rage in connected intelligent vehicles nowadays
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