Autonomous Driving Implementation in an Experimental Environment

TL;DR

This paper presents the development and testing of an autonomous driving system in a controlled experimental environment, focusing on lane tracking and obstacle avoidance using CNNs and various computer vision techniques.

Contribution

It introduces a comprehensive autonomous driving system with integrated lane detection and obstacle avoidance methods tested in a dedicated experimental setup.

Findings

Successful lane tracking using CNN models

Effective obstacle avoidance through computer vision techniques

System demonstrates autonomous behavior in controlled environment

Abstract

Autonomous systems require identifying the environment and it has a long way to go before putting it safely into practice. In autonomous driving systems, the detection of obstacles and traffic lights are of importance as well as lane tracking. In this study, an autonomous driving system is developed and tested in the experimental environment designed for this purpose. In this system, a model vehicle having a camera is used to trace the lanes and avoid obstacles to experimentally study autonomous driving behavior. Convolutional Neural Network models were trained for Lane tracking. For the vehicle to avoid obstacles, corner detection, optical flow, focus of expansion, time to collision, balance calculation, and decision mechanism were created, respectively.