Trajectory Prediction and Risk Assessment in Car-Following Scenarios Using a Noise-Enhanced Generative Adversarial Network

Traditional conflict analysis methods, relying on the assumption of constant velocity, often fall short in capturing the dynamic nature of driver behavior randomness during the interaction process. Predicting all potential collision trajectories proves crucial for comprehensive safety analysis. To address the challenge of accounting for trajectory randomness in car-following scenarios, this study introduces a noise-enhanced generative adversarial network, named Car-Following GAN, designed for predicting collision trajectories based on data from the Shanghai Naturalistic Driving Study (SH-NDS). The model employs an encoder-decoder framework, integrating a noise enhancement module to capture the intrinsic randomness of driving patterns. Demonstrating notable robustness across varying environmental conditions, our model showcases adaptability for trajectory prediction in diverse driving scenarios. A conflict measure, termed the Rear-end Collision Risk Index based on Car-Following GAN (RCRIC), is proposed to quantify the risk of a rear-end collision. Our approach conducts a comprehensive case analysis to assess the impact of various traffic risk factors on RCRIC. The results underscore that our noise-enhanced approach significantly improves the trajectory prediction accuracy of the model when compared to other noise addition methods. This enhancement is observed across various prediction time windows and under different weather conditions. Moreover, RCRIC, derived from the model employing our noise-enhanced approach, effectively mirrors the dynamics of rear-end collision risk by explicitly incorporating trajectory randomness into its assessment. Furthermore, the findings underscore the significant influence of light conditions, traffic density, and weather conditions on driving risk.