In crowd scenarios, predicting trajectories of pedestrians is a complex and challenging task
depending on many external factors. The topology of the scene and the interactions …

This paper provides a critical review on the state-of-the-art pedestrian and evacuation
dynamics so as to comprehensively comprehend the motion behaviors of pedestrians from …

Simulating crowds with realistic behaviors is a difficult but very important task for a variety of
applications. Quantifying how a person balances between different conflicting criteria such …

Human crowds have become hotspot research, particularly in crowd analysis to ensure
human safety. Adaptations of machine learning (ML) approaches, especially deep learning …

Deep reinforcement learning (DRL) has been proved to be more suitable than reinforcement
learning for path planning in large-scale scenarios. In order to more effectively complete the …

Diversity among agents' behaviors and heterogeneity in virtual crowds in general, is an
important aspect of crowd simulation as it is crucial to the perceived realism and plausibility …

Simulating believable virtual crowds has been an important research topic in many research
fields such as industry films, computer games, urban engineering, and behavioral science …

The combination of multi-agent technology and reinforcement learning methods has been
recognized as an effective way which is used in path planning-based crowd simulation …

This paper presents an approach for solving the crowd navigation problem in an unknown
and dynamic environment based on deep reinforcement learning. In our approach, we first …

Agent-based synthetic crowd simulation affords the cost-effective large-scale simulation and
animation of interacting digital humans. Model-based approaches have successfully …