A Computer Vision Approach for Autonomous Cars to Drive Safe at Construction Zone

TL;DR

This paper introduces a computer vision-based obstacle detection model for autonomous cars operating in construction zones, enhancing safety and reliability in complex driving environments.

Contribution

It presents a highly accurate YOLO-based obstacle detection model specifically designed for construction zones, with real-time performance and high precision.

Findings

Mean average precision over 94%

Inference time of 1.6 milliseconds

Robust detection under diverse drift conditions

Abstract

To build a smarter and safer city, a secure, efficient, and sustainable transportation system is a key requirement. The autonomous driving system (ADS) plays an important role in the development of smart transportation and is considered one of the major challenges facing the automotive sector in recent decades. A car equipped with an autonomous driving system (ADS) comes with various cutting-edge functionalities such as adaptive cruise control, collision alerts, automated parking, and more. A primary area of research within ADAS involves identifying road obstacles in construction zones regardless of the driving environment. This paper presents an innovative and highly accurate road obstacle detection model utilizing computer vision technology that can be activated in construction zones and functions under diverse drift conditions, ultimately contributing to build a safer road transportation system. The model developed with the YOLO framework achieved a mean average precision exceeding 94\% and demonstrated an inference time of 1.6 milliseconds on the validation dataset, underscoring the robustness of the methodology applied to mitigate hazards and risks for autonomous vehicles.