Wei Wu1, Weigong Zhang1,*, Dong Wang1, Lydia Zhu2, Xiang Song3
CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3811-3828, 2023, DOI:10.32604/cmc.2023.029787
Abstract An increase in car ownership brings convenience to people’s life. However, it also leads to frequent traffic accidents. Precisely forecasting surrounding agents’ future trajectories could effectively decrease vehicle-vehicle and vehicle-pedestrian collisions. Long-short-term memory (LSTM) network is often used for vehicle trajectory prediction, but it has some shortages such as gradient explosion and low efficiency. A trajectory prediction method based on an improved Transformer network is proposed to forecast agents’ future trajectories in a complex traffic environment. It realizes the transformation from sequential step processing of LSTM to parallel processing of Transformer based on attention mechanism. To perform trajectory prediction more… More >
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