Traffic Risk Assessment from Driving Scene Images

Different applications for advanced driver assistance systems (ADAS) have been increasing rapidly. With the advances of machine learning techniques, traffic risk assessment is becoming possible by understanding the complex driving scenes. This paper presents a technique to classify possible risks in four levels using deep neural networks. The important regions in road scene images are first predicted, followed by the risk assessment using a series of weighted RGB and optical flow images obtained from the proposed network model. To achieve better understanding of driving scenes, driver visual attention is incorporated to enhance the feature extraction. An LSTM model is then employed to learn the temporal information from road scene video clips. In addition to the experiments using the public TRA dataset, we also present a new LTDR dataset for performance evaluation. Compared with existing techniques, the result has demonstrated the effectiveness of the proposed method. The code is available at https://github.com/steven-wj-wu/RVL_Traffic_Risk_Assessment.