A Multi Model Autonomous Car Drive Recommendation Using Novel Lidar and Radar

Perception plays an essential part in autonomous driving, as it provides the ego vehicle with a fundamental understanding of its environment through sensors. Perception is difficult because of dynamic objects and continuously changing environments. The problem gets worse when adverse weather, including rain, sandstorms, fog, strong daylight, and snow, etc., interacting with the quality of perception. In this model, they have attempted to increase the accuracy of camera-based perception for tasks like object detection important for autonomous driving in adverse weather conditions. The autonomous vehicle is one of the challenging tasks in the automobile sector. In traditional driving, the human driver takes all control over the operation of the vehicle but in the autonomous vehicle, the human driver does not have any control, instead of controlling with the real-time decision. For the autonomous vehicle, path planning and the perception of the obstacles are very important. Therefore, by using Novel Multi Model Autonomous Car Drive Recommendation Using Novel Light Detection and Ranging (NLIDAR) and Radio Detection And Ranging (RADAR). This system includes its ability to perform high-resolution velocity imaging, detect objects in occlusion and over long ranges, and maintain robust performance in adverse weather conditions. The objects are detected and accidents avoided. This system shows better results interms of accuracy, accident avoidance, security, precision and recall.