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Research on Vehicle Trajectory Prediction Method for Intersections without Signal Lights
Abstract:
Urban road intersections are the most common complex traffic conditions, especially in intersections without signal lights, when a right-of-way conflict occurs between vehicles, the future trajectory of vehicles is full of uncertainty due to the driver’s personalized driving style and the difference in recognition of relevant driving rules. Accurate prediction of vehicle trajectory is of great significance to collision avoidance decision-making and path planning of ADAS and the driverless car. This article proposes a vehicle trajectory prediction method for intersections without signal lights, which combines the traditional vehicle Constant Turn Rate and Acceleration (CTRA) model in the Long Short-Term Memory (LSTM) network by attention mechanism, and the German open-source dataset inD for intersections without signal lights is used to verify the method studied in this article. The results show that the CTRA-LSTM model with the attention mechanism has higher prediction accuracy than the single CTRA model and LSTM model.