Human Trajectory Prediction in Autonomous Driving: A Comprehensive Survey

The advent of autonomous driving technology necessitates accurate prediction of human trajectories to ensure safety and efficiency in urban environments.This paper explores advanced methodologies for human trajectory prediction, focusing on the integration of machine-learning techniques and sensor fusion.We propose a novel framework that combines recurrent neural networks (RNNs) with attention mechanisms to enhance the prediction accuracy of pedestrian and cyclist movements.By leveraging data from LiDAR, cameras, and GPS, our approach dynamically adapts to the diverse and often unpredictable behavior of humans in traffic scenarios.We evaluate our model on several benchmark datasets, demonstrating significant improvements over existing state-of-the-art methods.The results indicate that our method can reliably forecast shortterm and long-term trajectories, contributing to the development of safer and more reliable autonomous driving systems.Future work will address scalability and real-time implementation to further bridge the gap between research and practical deployment.