Autonomous Driving of a Rover Based on Traffic Signals and Signs

Abstract

The traditional driving system has several disadvantages such as human error, driver fatigue and the inability to handle complex situations. These limitations make traditional driving unsafe and unreliable, leading to accidents and traffic congestion. The necessity for Autonomous Driving of a Rover based on Traffic Signals & Signs is to address these issues by automating the driving process and making it safer and more efficient. A dataset with traffic signs will be used to train a deep-learning model for classifying signs. A transfer learning technique will be used to deploy the trained model on the rover, considering hardware limitations. A camera on the rover captures images and sends them to the model for classification, enabling autonomous navigation based on traffic signs. The required software for the project includes Anaconda, a popular data science platform, and MaixPy, which is a version of MicroPython specifically designed for the Kendryte K210 chipset. The hardware required for the system includes the Zumo Shield for Arduino, which serves as the interface between the rover and the computer vision software, the Maixduino board, which is used to process the image data, and batteries to power the system. The system is designed to detect traffic signs and signals in real-time and respond accordingly, enabling the rover to navigate through traffic safely and efficiently.