Aggressive Driving Behavior Prediction Method Based on Attention Mechanism and Hierarchical Network

Abstract:

Multi-dimensional driving behavior data， including driver characteristics， road environment， and vehicle operation variables， were collected based on natural driving experiments. Then， the standard database of aggressive driving behavior was created after data cleansing and filtering. The significance analysis was used to obtain useful indexes， with which an eight-dimensional index set for aggressive driving behavior prediction was built. Finally， a two-layer time series model for aggressive driving behavior prediction was constructed. The first layer of the model is an artificial neural network. The second layer of the model is a long short-term memory （LSTM） network with an attention mechanism module. It is shown that the proposed model can increase the prediction accuracy by 10%；the two-layer structure and attention mechanism have a good improvement for the prediction accuracy （5% and 3%， respectively）.