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.

The integration of vision and radar sensors is crucial for enhancing the perception and decision-making capabilities of autonomous cars. This survey paper reviews state-ofthe-art techniques in vision-radar fusion, categorizing and analyzing early, late, and hybrid fusion strategies. We discuss the strengths and limitations of individual sensors and how their complementary characteristics can improve overall system performance. Key topics include data preprocessing, feature extraction, and sensor alignment, as well as machine learning and deep learning approaches for effective fusion. Practical challenges such as sensor calibration, synchronization, and heterogeneous data handling are addressed. We evaluate fusion techniques in real-world scenarios, assessing their accuracy, robustness, and computational efficiency. This survey aims to guide researchers and practitioners in developing more reliable and efficient autonomous driving systems through advanced vision-radar fusion techniques, offering insights into future research directions and potential improvements.