Lidar Based Detection,Tracking And Motion Planning For Autonomous Ground Vehicle

With the rapid development of computer technology and automobile technology,driverless vehicle has become a reality step by step.The main research directions are environmental perception,decision-making planning and motion control.Environmental perception can be further divided into detection,tracking and recognition.Lidar has become the main sensor of environmental perception because of its advantages of high measurement accuracy and little influence by light.Device.In order to further improve the safety and stability of driverless vehicles on complex urban structured roads,this paper presents a method of physical detection based on lidar,a method of multi-target tracking based on Hungarian algorithm and a method of motion planning based on vehicle dynamics constraints.Firstly,a detection method based on lidar is proposed to solve the problems of long operation time and sensitivity to noise in object detection.This paper chooses VLP-16 lidar,understands its working principle and rearranges point clouds in spatial order.In order to adapt to the characteristics of close and distant point clouds of lidar,using the angle as the detection standard,the continuity of road slope is validated for road segmentation,and then the differences of angles between objects are compared for clustering.The breadth-first traversal method is used to improve the rapidity of the algorithm.Then,for the clustered point clouds,the bounding frames with orientation are extracted and the coordinates of the center points are calculated.Finally,a visual program is written to verify the accuracy of object detection.Then,aiming at the problems of missing detection and occlusion,a multi-target tracking method based on Hungarian algorithm is proposed.Firstly,the coordinates of the central points of the object detection are taken as the aiming points,and the cumulative weighted values of the size,position,number of interior points and speed of the object are taken as the judging criteria.Then,the Hungarian algorithm is used to correlate the data of the front and back frames with the idea of maximum matching of the bipartite graph.Then,Kalman filter is used to filter the predicted value of the previous frame and the detected value of this frame,and the estimated value of this frame object is obtained.Then,the estimated value is put into the trajectory,and the unsuccessful trajectory is predicted continuously in order to catch up with the missing object again,and a new trajectory is added to the unsuccessful object.Finally,the visualization experiment is carried out.So far,the trajectories of other objects in the environment are obtained.Finally,aiming at the problem that the motion control module is difficult to track the trajectory,a motion planning method based on vehicle dynamics is proposed.Firstly,vehicle dynamics simulation software veDYNA is used to intuitively understand the characteristics of vehicle under-steering.By establishing a two-degree-of-freedom lateral dynamics model and a Dugoff tire model,the relationship between minimum steering radius,road adhesion coefficient and longitudinal vehicle speed is quantitatively analyzed.Then make simple decision instructions in the scenario of ideal structured roads.Then,the motion planning is divided into path-velocity decoupling planning.According to the arc-changing model,the preview points are obtained.Then,the path is fitted by cubic spline interpolation,and the velocity is planned to obtain the trajectory sequence points on the premise of satisfying the constraints.Finally,the algorithm is verified on the Udacity simulation platform.