Vehicle trajectory prediction in weaving area of expressway based on transfer learning - YIN Zijian; XU Liangjie; LIU Wei; MA Yukang; LIN Hai (Shenzhen Daxue xuebao. Ligong ban)
Vehicle trajectory prediction in complex highway weaving areas plays a crucial role in the decision-making and control of intelligent vehicles. To address the challenges of agility and accuracy issues for trajectory prediction brought by the complex traffic flow in weaving areas, we propose a vehicle trajectory prediction method based on transfer learning. By utilizing an existing highway straight-line segment trajectory prediction model for transfer learning training, faster and more accurate trajectory predictions can be achieved in weaving areas. Leveraging trajectory data from the next generation simulation (NGSIM) dataset in weaving areas, a long short-term memory (LSTM) neural network model is adapted through transfer learning, building upon the well-trained highway straight-line segment model. Furthermore, a rolling prediction method is adopted for frame-by-frame precise trajectory prediction in time series. The experimental results show that the accuracy of lateral and longitudinal behavior prediction can reach 98.35% and 93.01%, respectively, and the root mean square error of trajectory prediction is 2.04 cm. Transfer learning in the weaving area can shorten the model training time by 61.1%, while simultaneously improving prediction accuracy and model generalization capabilities.