A performance comparison of YOLOv8 models for traffic sign detection in the Robotaxi-full scale autonomous vehicle competition.

The ability to recognize traffic signs is a critical skill for safe driving, as traffic signs provide drivers with essential information about the road conditions, potential hazards, speed limits, and other important details that can impact their driving. By recognizing and understanding traffic signs, drivers can react quickly and appropriately to different situations on the road, which helps prevent accidents and ensures the safety of all road users. As part of the Robotaxi-Full Scale Autonomous Vehicle Competition, a project was undertaken to develop a traffic sign recognition system using YOLOv8, a state-of-the-art deep learning model that can detect and classify objects in real-time. The project team trained YOLOv8 on a dataset of traffic sign images to create a model that could accurately recognize and classify different types of traffic signs. This traffic sign recognition system has the potential to significantly improve road safety by helping autonomous vehicles and human drivers to better understand their surroundings and react appropriately to changing road conditions. The system can assist drivers by providing real-time alerts and warnings about potential hazards, speed limits, and other important information. Furthermore, this project demonstrates the power and potential of deep learning and artificial intelligence in improving transportation safety and efficiency. As AI technology continues to advance, we can expect to see more innovative applications in the automotive industry that will help improve the driving experience and make our roads safer for everyone. [ABSTRACT FROM AUTHOR]