Early Traffic Accident Anticipation via Feature Consistency Representation and Soft Label Regression

Early traffic accident prediction using dashcam videos plays a crucial role in enhancing the safety of intelligent vehicles. Accurately predicting accidents in advance can significantly reduce traffic accidents and improve overall road safety. However, despite extensive research efforts to capture more visual information by employing different feature extraction methods within the same frame, the consistency between features within the same frame and the discrepancy between features across different frames have not been sufficiently emphasized. To address this critical issue, we introduce contrastive learning into the field of accident prediction and propose a novel feature fusion module for the deep integration of diverse features. Our method treats features from the same frame as positive pairs, neighboring frames as sub-positive pairs due to their high correlation, and features from temporally distant frames as negative pairs. This approach effectively strengthens the representation capability of the model, thereby improving overall predictive performance. Additionally, we redefine the accident prediction task by converting it into an anomaly score regression problem using soft labels. This redefinition allows the model to better quantify the likelihood of an accident, offering a more nuanced and accurate prediction. We evaluate our method comprehensively on two publicly available Dashcam Accident Dataset (DAD) and Car Crash Dataset (CCD) datasets to assess its performance. The results demonstrate that our method outperforms state-of-the-art accident prediction approaches, highlighting its potential for practical applications. Code will be available at https://github.com/yangugu/TAP_CC .