The Implementation of traffic sign recognition on the scaled vehicle model
Abstract
The popularity of autonomous vehicles has grown in the past few years as autonomous systems are more and more present on vehicles. The most accessible way for students of mechanical and software engineers to learn about autonomous vehicles is by applying algorithms and systems necessary for autonomous driving on the scaled vehicle model. These models are, as in this case, and are equipped with all systems necessary for autonomous driving, such as a four-wheel drive powertrain, a suspension system, an electrically controlled steering system, a brain-computer and a camera. The goal of projects such as this one is to make the vehicle capable of autonomous driving on a designated track, obeying regular traffic rules and signs (for example, the vehicle has to perform a full stop when it approaches the stop sign). To make this possible, it is necessary for a vehicle to “know” which traffic sign is nearby, i.e., traffic sign recognition is required. For this purpose, traffic sign recognition is done by an artificial neural network. The training process of the proper artificial neural network will be shown in this paper.
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