AI-based Vehicle Navigation with Real-Time Traffic and Weather Insights

In the fast-changing world of smart transport, self-driving cars are becoming an important part of making city travel safer and smoother. One of the toughest challenges they face is how to quickly make the right driving decisions when traffic jams or bad weather suddenly appear. Most regular navigation systems only follow fixed routes or make small changes, but they don’t fully adjust to live conditions, which can cause delays, unsafe roads, or wasted fuel. Our project, AI-Powered Route Optimization, is built to solve this by using live information from traffic data, weather updates, sensors, and cameras on the vehicle. With this, the system can spot blocked roads, track traffic build-up, and understand risks like rain, fog, or accidents. Machine learning models predict traffic patterns, while computer vision with OpenCV and YOLO helps detect weather effects and road obstacles. By combining this with Google Maps navigation, the vehicle can pick the best possible path at every moment instead of sticking to one pre-decided route. This makes driving not only faster but also safer and more energy-friendly. In the long run, the system aims to support a cleaner, more efficient, and smarter way of managing urban transport in real life.