Pedestrian Simulation

Pedestrian simulation aims to create realistic virtual representations of pedestrian behavior for various applications, primarily focusing on improving safety and efficiency in shared spaces with autonomous vehicles and other mobile robots. Current research emphasizes developing more accurate and diverse pedestrian models, incorporating uncertainty in predictions, and leveraging techniques like deep reinforcement learning, generative adversarial networks, and vision transformers to achieve greater realism and controllability. These advancements are crucial for validating autonomous systems, optimizing urban planning, and understanding phenomena like crowd dynamics and disease transmission in densely populated areas.