Mixed Traffic Flow Trajectory Prediction Method Driven by Domain Knowledge and Data

Abstract:

Autonomous vehicles need to have the ability to predict the trajectory of vehicles around them. There are many mixed traffic flow roads with weak rules and strong interactions in developing countries， and trajectory prediction of high-density traffic flows is an extremely challenging task. In order to predict the trajectory with accuracy and interpretability for mixed traffic flow， a domain knowledge-guided convolutional long short-term memory （DK-Conv-LSTM） to realize the long and short-term trajectory prediction was proposed. In the data-driven model， a convolutional layer （Conv） was used to extract crucial information from interactive vehicles， and the long short-term memory （LSTM） was utilized to predict trajectory after the concatenation of the history information of the vehicle. Knowledge expertise guided the training of deep learning models by being embedded in loss functions. Using the basic LSTM as the benchmark， the Conv-LSTM with only the convolutional structure added， reduces the final displacement error （FDE） by 30.46 % and the average displacement error （ADE） by 34.78 %. The DK-Conv-LSTM reduced the FDE by 46.81 % and ADE by 49.08 %. Moreover， it could recreate complex driving behavior trajectories such as following between two vehicles and overtaking.