In recent years, vehicle detection in intelligent transportation systems using 3D LIDAR point clouds based on deep neural networks has made substantial progress. However, when the point clouds are very sparse, the detection model cannot generate proposals efficiently, resulting in false negative results. Considering that the object tracking technology accurately predicts vehicles based on historical measurements and motion models, and these prediction results can become proposals for object detection. Therefore, this paper proposes a novel object detection paradigm based on tracking feedback to address the false negative problem based on sparse point clouds. According to the distribution of the state vector from the Kalman prediction, multiple proposals are sampled and fed back to the second stage of two-stage detection models. After regression and non-maximum suppression, the false negative results can be effectively reduced. This method enhances the vehicle detection capability of classical neural networks. Comparing the recall metric of multiple detection models at different distances in the public KITTI and nuSences datasets, the proposed method can promote up to 5.31% compared to the previous method, which reflects the effectiveness and versatility of the proposed method.

Real-time and accurate ground segmentation is a crucial technology for intelligent vehicles. On the one hand, it obtains drivable area information for vehicles, which is of great significance for subsequent navigation and control. On the other hand, it is beneficial for improving the performance of object detection and segmentation. In this paper, Hy-Seg is proposed, which is a hybrid method and achieves accurate and fast ground segmentation. First, to improve the efficiency of the algorithm, the 3D point cloud generated by LIDAR is represented by a polar grid map. Then, candidate ground points in each grid are generated by a gradient-based method, reducing most of the false-positive results. Polynomial fitting based on random sample consensus is used to model the ground of each segment correctly. Finally, the original 3D point cloud is segmented using the fitted model. Experiments on SemanticKITTI show that the proposed method can not only achieve an accurate segmentation result but also run at a frequency of 53 Hz, which meets the requirements of intelligent vehicles.

As one of the most important tasks in autonomous driving systems, ego-lane detection has been extensively studied and has achieved impressive results in many scenarios. However, ego-lane detection in the missing feature scenarios is still an unsolved problem. To address this problem, previous methods have been devoted to proposing more complicated feature extraction algorithms, but they are very time-consuming and cannot deal with extreme scenarios. Different from others, this paper exploits prior knowledge contained in digital maps, which has a strong capability to enhance the performance of detection algorithms. Specifically, we employ the road shape extracted from OpenStreetMap as lane model, which is highly consistent with the real lane shape and irrelevant to lane features. In this way, only a few lane features are needed to eliminate the position error between the road shape and the real lane, and a search-based optimization algorithm is proposed. Experiments show that the proposed method can be applied to various scenarios and can run in real-time at a frequency of 20 Hz. At the same time, we evaluated the proposed method on the public KITTI Lane dataset where it achieves state-of-the-art performance. Moreover, our code will be open source after publication.

ABSTRACT In this paper, we proposed a simple approach to analyse the efficiency and propulsive characteristics of the high-altitude propeller in accordance to the Vortex Lattice Lifting line Method (VLM) theory, which is commonly used in preliminary design and parametric studies of propeller propulsion. The Computational Fluid Dynamics (CFD) method was used to obtain aerofoil aerodynamic data. The optimal pitch angle and propeller blade chord length (along the radial direction) can be calculated using the information from the database. The propeller wake model sees helical slipstreams applied to both lightly and moderately loaded propellers. The proposed method is capable of identifying the optimal efficiency through varying the number of propeller blades, radius and the rotational speed. The relationship between the optimal efficiency and design parameters is then established. This method was verified using CFD calculations.