Learning the Dynamics of Human Patterns for Autonomous Navigation

The state of autonomous driving, robot navigation, and athletic activities is very significant in modern society due to humankind's ongoing advancement. When it comes to mobile robots or automated vehicles operating within congested areas, mobile communication systems are essential components for autonomous navigation aimed at preventing accidents involving people. This article presents a deep learning (DL) and hybrid multiprocessor-based approach for estimating human posture using biological signal processing. The main objective of this article is to develop an algorithm to recognize human poses for the purpose of protecting human beings from collision with higher accuracy to assist robots or autonomous vehicles in safe and efficient navigation in crowded environments. This study presents a transformer-based framework that uses input characteristics such as human locations, face orientations, and crucial 2D skeleton points using integrated within-the-wild sensory feedback to anticipate human potential paths in human-centric situations. We will identify 18 distinct locations within the human skeleton for pose estimation in real time, which will help autonomous navigation operations for mobile robots or autonomous vehicles avoid collisions with humans in a crowded environment. This proposed research implements the Python programming language to demonstrate the performance of this approach, and the outcomes of simulations validate its reliability and accuracy.