ABSTRACT: Driving anger detection is an important topic in traffic safety analysis. Driving anger is getting more and more prevalent and serious in everyday traffic incidents. Many deaths and injuries are related to road rage. Aggressive driving behaviors create kinds of roadway traffic safety hazards. Mitigating potential risk caused by road rage is indispensable to increase overall level of traffic safety.
This paper puts forward an integrated computer vision model composed of patter recognition and 14 layers very deep convolutional neural network to recognize driver anger emotion and classify angry driving from natural driving status. Histogram of gradients method was applied to locate faces. Multidimensional analysis in calculating geometric pattern of facial appearance was used to classify driving anger from natural status. To mitigate false call problem, threshold of classifying images into natural was tuned up and images that were classified into anger were all sent to deep learning model to recheck. Driver anger detection algorithm with overall accuracy rate of 86.7% was achieved in the simulator environment. Test scenario was established and Tongji University 8 Degree of Freedom driving simulator was used to collect data from 30 recruited drivers.
Bowen Cai, Xuxin Zhang, Xuesong Wang. Driving Anger Recognition Based on Convolutional Neural Network. Transportation Research Board 99th Annual Meeting, Washington D.C., USA, 2020. 1.12-16.