Simulating LIDAR Point Cloud for Autonomous Driving using Real-world Scenes and Traffic Flows12/14/2023 Finally, the results of the evaluations on the KITTI datasets show that the proposed approach enables both object detection and distance estimation. The main purpose of the conducted research is to fuse sensor data to estimate the distance of objects detected using Tiny YOLOv4. Both sensors have varied different characteristics and must be aligned by performing a geometrical transformation and projection to fuse the sensor’s data. To fully exploit the benefits of Lidar's depth information and vision's obstacle classification capabilities, this paper presents an object detection and distance estimation via Lidar and camera fusion. ![]() ![]() Classic vision-based cars identification approaches are insufficiently accurate, particularly for small objects, whereas sensors such as Lidars help in detecting objects in all shapes and sizes but still limited in classifying and recognizing detected obstacles. One of the most main perception challenges for autonomous vehicles is cars detection.
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