Traffic Sign Detection using Clara and Yolo in Python

Research in the field of self-driving and autonomous vehicles is continuously growing. Many researchers are working to make self-driving cars more secure. Researchers work diligently to make cars as safe as possible to minimize fatal injuries on our roads. In about 30 percent of these incidents, there is a central factor: speed. For several decades, speeding has been a widely discussed subject for major automotive companies. The purpose of the project is to contribute to this research by implementing a driving simulator for a device that can understand speed limit signs and make decisions that make the driver more comfortable and safer to drive. The CARLA Learning to Act (CARLA), an open-source autonomous test simulator consisting primarily of two modules, the CARLA simulator and the Python API module, is used as a simulator in this analysis. The algorithm You Look Just Once (YOLO) is used to classifying road signs. Yolo sees the whole picture during training and testing, encoding contextual information about the groups of objects and their appearances instead of a sliding window over several places in an image. This feature makes it extremely easy to analyze an image. Several utilities may be used to identify the speed signs of a road. There are two applications for this project: a request for a notice warning the driver that the vehicle's speed is above the maximum allowed speed of that traffic, and a request for a rule to reduce the vehicle's speed when the traffic limit is reached.