This research articulates the development and implementation of an advanced LiDAR-based framework for the detection and tracking of humans within autonomous systems, significantly enhancing situational awareness in dynamic environments. Leveraging high-resolution 3D point cloud data, the study introduces a novel algorithmic approach that integrates ground plane removal, Euclidean and DBSCAN clustering, and principal component analysis (PCA) to robustly identify and track human figures across varying conditions and occlusions. This framework is validated through a series of field tests with a Velodyne HDL-32 LiDAR system, demonstrating improved accuracy and computational efficiency in real-time human tracking. The findings underscore LiDAR’s pivotal role in augmenting the safety and reliability of autonomous vehicles, robotics, and surveillance systems by effectively managing the complexities of real-world operational settings. Our pilot study provides a scalable model for future enhancements in autonomous navigational technologies.