Road segmentation for autonomous vehicle: A review

Autonomous cars will reduce the quantity of street injuries because of human mistakes. Traditionally, a step by step advancement has been introduced in intelligent vehicles. These advancements boom the automation stage in motors with systems that assist the driving force in order to keep a steady speed, comply with a lane, or perform vehicle- driver control handover. Autonomous cars require deep information about their surroundings. Thus it's very vital to carry out road segmentation and object detection for identifying the drivable area in route planning. A portion of road with uniform characteristics can be represented specifically as segments. Existing road segmentation algorithms basically depends on quality of image from camera due to light conditions. As well one more issue for existing road segmentation depends on high quality of lane markings. LiDAR sensors can attain the 3D geometry information of the vehicle surroundings with excessive precision. The information collected by LiDAR will increase precision at the cost of increased memory usage and hence computational overhead. A brand new method using both the Camera and LiDAR data fusion for road detection is discussed. It decreases the gap among the point cloud of 3-D LiDAR information.