LAUSR

Fast and accurate prediction of pedestrian trajectory around vehicles can reduce or even prevent most traffic accidents and improve the safety of traffic participants. This involves the real-time interaction and fusion of various information, such as the vehicle’s motion characteristics, the pedestrian’s historical motion trajectory, and the motion relationship between people and cars. However, most of the existing algorithms use RNN as the skeleton to process the information prediction trajectory, which is weak in extracting the internal relationship between different information, and the running time of the algorithm is long. To solve these problems, we propose a Transformer based deep learning algorithm (CVTF) to complete the first-person perspective pedestrian trajectory prediction task. We have the following innovations about this model: 1: We use the stamp coding method for vehicle speed and pedestrian information to ensure we can learn the characteristics of different information sources. 2: Transformer structure is used, and its attention mechanism is improved (Maybe-self attention), which improves the running speed of the model and reduces the memory consumption. 3: Combined with Conditional Variational Autoencoder (CVAE), hidden variables are introduced to improve the prediction accuracy. Experiments on three pedestrian trajectory prediction benchmarks show that our model achieves the most advanced performance.