Real-Time Run-Off-Road Risk Prediction Based on Deep Learning Sequence Forecasting Approach

Abstract

Driving risk prediction is crucial for advanced driving technologies, with deep learning approaches leading the way in driving safety analysis. Current driving risk prediction methods typically establish a mapping between driving features and risk statuses. However, status prediction fails to provide detailed risk sequence information, and existing driving safety analyses seldom focus on run-off-road (ROR) risk. This study extracted 660 near-roadside lane-changing samples from the high-D natural driving dataset. The performance of sequence and status prediction for ROR risk was compared across five mainstream deep learning models: LSTM, CNN, LSTM-CNN, CNN-LSTM-MA, and Transformer. The results indicate the following: (1) The deep learning approach effectively predicts ROR risk. The Macro F1 Score of sequence prediction significantly surpasses that of status prediction, with no notable difference in efficiency; (2) Sequence prediction captures risk evolution trends, such as increases, turns, and declines, providing more comprehensive safety information; (3) The presence of surrounding vehicles significantly impacts lane change duration and ROR risk. This study offers new insights into the quantitative research of ROR risk, demonstrating that risk sequence prediction is superior to status prediction in multiple aspects and can provide theoretical support for the development of roadside safety.