Review of Research on Trajectory Prediction of Road Pedestrian Behavior

In path planning for shared spaces between autonomous vehicles and pedestrians, accurate and efficient pedestrian trajectory prediction is critical for ensuring road safety. Pedestrian trajectory prediction not only relies on historical behavior data but also requires a comprehensive consideration of the complex dynamic interactions between pedestrians and vehicles, traffic infrastructure, and multi-directional vehicles. Significant advancements have been made in this field in recent years, making it a focal point of research. This paper provides a systematic review of the current research. Firstly, it defines the core concepts of pedestrian trajectory prediction and conducts an in-depth analysis of the main prediction methods. It then comprehensively outlines the primary data sources for pedestrian behavior, including LiDAR, cameras, and other multimodal sensing devices, while exploring key feature extraction methods, such as pedestrian motion features, contextual scene characteristics, the impact of traffic infrastructure, etc. Based on these data, this paper systematically reviews both physics-based and data-driven prediction approaches, with a focus on the development of statistical models, deep learning, and reinforcement learning models. Special emphasis is placed on deep learning methods, categorized by network architecture into sequential models, convolutional neural networks, graph convolutional networks,  generative adversarial networks, etc. This paper also reviews commonly used datasets and evaluation metrics in the field, providing a thorough evaluation of current algorithmic performance. Finally, it addresses the challenges in pedestrian trajectory prediction for autonomous driving, particularly the dynamic coupling between pedestrians with multi-directional traffic and infrastructure, offering potential solutions and discussing future research directions.