Advanced Collision and Obstruction Detection and Prevention using ESP-32 & Deep Learning.

Abstract

With the increasing complexity and congestion of road networks and the growing diversity of transportation modes, the need for effective safety measures has become paramount. In response to this imperative, this study delves into a comprehensive analysis of cutting-edge technologies, methodologies, and systems designed to mitigate collision risks and prevent obstructions in various domains of transportation, including automotive, aviation, maritime, and rail. This research paper presents a novel approach to enhancing safety in diverse transportation systems by leveraging the capabilities of the ESP32 microcontroller in tandem with state-of-the-art deep learning algorithms, namely YOLO (You Only Look Once) and Machine learning (ML) techniques. The study explores the integration of these cutting-edge technologies to provide efficient and effective collision and obstruction detection and prevention mechanisms across various modes of transportation, including road vehicles, drones, and autonomous robots.