Dynamic Vehicle Graph Interaction for Trajectory Prediction Based on Video Signals

The roadside video surveillance signal can help people achieve vehicle tracking and trajectory generation. Using these trajectories can learn the future motion of vehicles. Existing prediction methods can not analyze the interaction between vehicles well. To this end, we design a dynamic vehicle graph to represent the dynamic interaction between vehicles for trajectory prediction. A graph convolution module with an attention mechanism is used to extract the feature of the dynamic vehicle graph. Experiments comparing with baseline methods are conducted on a real-world dataset to demonstrate our model’s improvement in forecast accuracy.