Personalized Trajectory-based Risk Prediction on Curved Roads with Consideration of Driver Turning Behavior and Workload

Accurate and robust risk prediction on curved roads can significantly reduce lane departure accidents and improve traffic safety. However, limited study has considered dynamic driver-related factors in risk prediction, resulting in poor algorithm adaptiveness to individual differences. This article presents a novel personalized risk prediction method with consideration of driver turning behavior and workload by using the predicted vehicle trajectory. First, driving simulation experiments are conducted to collect synchronized trajectory data, vehicle dynamic data, and eye movement data. The drivers are distracted by answering questions via a Bluetooth headset, leading to an increased cognitive workload. Secondly, the
k
-means clustering algorithm is utilized to extract two turning behaviors: driving toward the inner and outer side of a curved road. The turning behavior of each trajectory is then recognized using the trajectory data. In addition, the driver workload is recognized using the vehicle dynamic features and eye movement features. Thirdly, an extra personalization index is introduced to a long short-term memory encoder–decoder trajectory prediction network. This index integrates the driver turning behavior and workload information. After introducing the personalization index, the root-mean-square errors of the proposed network are reduced by 15.6%, 23.5%, and 29.1% with prediction horizons of 2, 3, and 4 s, respectively. Fourthly, the risk potential field theory is employed for risk prediction using the predicted trajectory data. This approach implicitly incorporates the driver's personalized information into risk prediction.