Design Of Intelligent Road Eye Using AI And Machine Learning For Automobiles

The project aims to design an intelligent road eye by using AI and a machine learning approach to detect speedbumps and potholes on the road. The system design utilizes a YOLOv5 custom-trained model and COCO dataset in detecting the objects on the road. The system is integrated with lane detection algorithms to achieve active steering feedback and pothole avoidance. Based on the detection results, feedback will be given in the form of visual, audio, and steering angles, allowing the driver to have sufficient response time to perform braking or steering adjustments where applicable. The trained model can achieve a mean average precision value (mAP) of up to 0.995 for all classes, and a maximum detection range of 5.77m and 34.8m for potholes and speedbump respectively. The future works of the project include integrating the algorithm into the vehicle to achieve autonomous braking and active pothole avoidance with the help of sensors and cameras on the vehicle, as well as adopting augmented reality (AR) to project the visual feedback on the vehicle windscreen.