Vehicle Colour, Speed and Type Prediction

This research presents a novel method that combines machine learning and computer vision for simultaneous vehicle speed estimation, color detection, and mood estimation. Our system aims to improve road safety and optimize traffic control by leveraging radar and lidar technologies for accurate speed estimation, while advanced deep learning algorithms handle color detection and mood prediction of drivers. Through extensive testing, we achieved high accuracy in speed estimation, with an average error margin of less than 5%, and vehicle color detection accuracy exceeding 95%. Additionally, our mood prediction model demonstrated a significant correlation with driver behavior, showcasing its potential to predict risk factors on the road. The results confirm the feasibility and effectiveness of our approach, revealing its potential to transform traffic management practices. Furthermore, we propose future research directions to integrate vehicle type prediction into our framework, paving the way for smarter, more adaptive, and safer transportation solutions.