LiDAR sensors are used as core sensors in autonomous vehicles. The range of use varies from positioning to recognition. In this paper, we introduce a method that can utilize the LiDAR point cloud for recognition and positioning. In the positioning section, after detecting the lane with a LiDAR, the positioning information is corrected using this information. First of all, lane detection using LiDAR has the advantage of being able to instantly determine the three-dimensional position of the lane, and the detection performance is not affected by the illuminance. Lane detection using LiDAR detects all road markings on the ground and preprocesses them to provide them as input to a multi-layer perceptron-based network to instantly obtain the three-dimensional position of points corresponding to lanes. In this way, IMU is corrected by fusing lane information detected in real time and lane information obtained using the yaw rate from the vehicle's IMU. Finally, we propose a Kalman Filter-based sensor fusion complex positioning system that corrects the absolute position estimated by the GPS absolute position and the corrected yaw rate in conjunction with high definition map.