Paper Title
"Integrating Deep Learning in Urban Traffic Management: A Comprehensive Review of Pedestrian Behavioral Analysis and Trajectory Prediction at Crossings"

With growing numbers of intelligent autonomous systems in human environments, such systems' ability to perceive, understand, and anticipate human behavior becomes increasingly important. Explicitly, predicting future locations of dynamic agents and planning considering such predictions are critical tasks for self-driving vehicles, service robots, and advanced surveillance systems. Autonomous driving is an active area of research and includes many issues related to navigation and trajectory prediction. To perform efficient and collision-free navigation, we need accurate trajectory prediction capabilities. Trajectory prediction is the problem of predicting the short-term and long-term spatial coordinates of various road-agents such as cars, buses, pedestrians, rickshaws, and even animals; furthermore, TP approaches are limited to short-term predictions and cannot handle a large volume of trajectory data for long-term prediction. Autonomous cars driving in urban environments are challenging because autonomous vehicles require the ability to communicate with other road users and understand their intentions. Such interactions are essential between cars and pedestrians as the most vulnerable road users. However, understanding pedestrian behavior is not intuitive and depends on factors such as demographics of pedestrians, traffic dynamics, environmental conditions, etc. This review paper is focused on proposing a Unified Deep Learning Approach for pedestrian's behavior Learning and its trajectory prediction Keywords - Human Environments, Traffic Dynamics