A Review on Object Detection for Autonomous Vehicles

The automation industries have been developing since the first demonstration in the period 1980 to 2000 it is mainly used on automated driving vehicle. Now a day’s automotive companies, technology companies, government bodies, research institutions and academia, investors and venture capitalists are interested in autonomous vehicles. In this work, object detection on road is proposed, which uses deep learning (DL) algorithms. You only look once (YOLO V3, V4, V5). Object detection is an essential component of perception in autonomous vehicles, forming the basis for navigation, collision avoidance, and scene understanding. Additionally, recent fusion techniques combining LiDAR, radar, and visual data, such as MV3D and AVOD, offer improvements in occlusion handling and distance estimation, contributing to better performance in real-world scenarios. The challenges of object detection under adverse conditions, including low-light and inclement weather, are also addressed by techniques such as robust multi-sensor fusion and novel data augmentation methods.