Context CVGN: A conditional multimodal trajectory prediction network based on scene semantic modeling

Pedestrian behavior and trajectory prediction in highly dynamic and interactive scenes have emerged as among the most daunting challenges in the realm of autonomous driving. In addressing the modeling of pedestrian interaction and the generation of multimodal trajectories for pedestrian trajectory prediction, we present a novel approach: a context-based conditional variational generative adversarial network (Context-CVGN). This network is capable of capturing the physical environment, pedestrian interactions, and other scene elements by representing them as a bird's-eye view (BEV) semantic map. It can then infer various potential pedestrian trajectories in the future. By training and evaluating our model on the ETH&UCY dataset, we demonstrate superior performance compared to several state-of-the-art methods, particularly in terms of the final displacement error (FDE). These results substantiate the efficacy of our model in accurately predicting future pedestrian trajectories.