Autonomous Vehicle: Obstacle Avoidance and Classification with YOLO

The development of autonomous vehicles, especially in the context of self-driving cars, depends heavily on object detection and classification. In order to provide safe and effective navigation, this process entails recognizing and classifying various things in the surroundings of the vehicle. The system is capable of real-time detection of pedestrians, cars, road signs, and other potential obstructions. The self-driving automobile is able to detect its environment accurately and make safe navigation decisions by combining sensor data from cameras. Robustness to changing environmental conditions, occlusions, and the demand for quick processing to satisfy real time requirements are among the challenges. In order to improve road safety and dependability, this abstract emphasizes the importance of object detection and classification in the development of self-driving technology.