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Due to much imperfect detection performance of onboard sensors in dense driving scenarios, the accurate and explicit perception of surrounding objects for Advanced Driver Assistance Systems and Autonomous Driving is challenging. This paper proposes a novel detection-level fusion approach for multi-object perception in dense traffic environment based on evidence theory. In order to remove uninterested...
The ability to perform long-range pedestrian detection is essential for autonomous vehicles. However, for 3-D LIDAR, an object's point cloud becomes sparse when it is away, directly affecting its detection as a result. In this paper, a novel density enhancement method is proposed to improve the quality of a sparse point cloud. The input of the method is an object's raw point cloud; first, a high-quality...
A target selection method based on multi features fusion is proposed to improve the accuracy of target vehicle selection. The parameters consisting of the longitudinal distance, lateral distance, relative speed between objects and the host vehicle, the in-lane probability of objects are regarded as the features of individual vehicles. Firstly, some pre-processes of features data are carried out including...
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