A Follow-Up Risk Identification Model Based on Multi-Source Information Fusion

To address poor real-time performance and low accuracy in car-following risk identification, a model based on autoencoders is proposed. Using the SHRP2 natural driving dataset, this paper constructs a car-following risk identification model in two stages. In Stage 1, a deep feedforward neural network autoencoder reconstructs preprocessed multi-source heterogeneous indicators of human-vehicle-road-environment. The high-dimensional latent space feature representation is used as input for Stage 2, enhancing the basic model’s performance. Eight basic models and sixteen models with autoencoders are compared using multiple evaluation indicators. A simulated driving test verifies the model’s generalization and robustness. Results show improved accuracy in car-following risk identification, with the optimized AutoEncoder_LR performing best at 91.33% for risk presence and 70.14% for risk levels. These findings can aid in safe driving and rear-end accident prevention.