Spatio-Temporal-Attention-Based Vehicle Trajectory Prediction Considering Multi-Vehicle Interaction in Mixed Traffic Flow
Source abstract
Abstract
As a link connecting the environmental perception system and the decision-making system, accurate obstacle trajectory prediction provides a reliable guarantee of correct decision-making by autonomous vehicles. Oriented toward a mixed human-driven and machine-driven traffic environment, a vehicle trajectory prediction algorithm based on an encoding–decoding framework composed of a multiple-attention mechanism is proposed. Firstly, a directed graph is used to describe vehicle–vehicle motion dependencies. Then, by calculating the repulsive force between vehicles using a priori edge information based on the artificial potential field theory, vehicle–vehicle interaction coefficients are extracted via a graph attention mechanism (GAT). Subsequently, after concatenating the vehicle–vehicle interaction feature with the encoded vehicle trajectory vectors, a spatio-temporal attention mechanism is applied to determine the coupling relationship of hidden vectors. Finally, the predicted trajectory is generated by a gated recurrent unit (GRU) decoder. The training and evaluation of the proposed model were conducted on the NGSIM public dataset. The test results demonstrated that compared with existing baseline models, our approach has fewer prediction errors and better robustness. In addition, introducing artificial potential fields into the attention mechanism causes the model to have better interpretability.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Autonomous Vehicle Technology and Safety · Traffic Prediction and Management Techniques · Traffic and Road Safety
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
China Merchants Testing Vehicle Technology Research Institute Co., Ltd., Chongqing 401329, China
Chongqing Key Laboratory of Industry and Informatization of Automotive Active Safety Testing Technology, Chongqing 401329, China
College of Engineering and Technology, Southwest University, Chongqing 400715, China
College of Artificial Intelligence, Southwest University, Chongqing 400715, China
Funding
Science and Technology Innovation Key R&D Program of Chongqing · National Key Research and Development Program of China · Open Fund of the Chongqing Key Laboratory of Industry and Information of Automotive Active Safety Testing Technology
References (35)
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