Robust LiDAR-Based Vehicle Detection for On-Road Autonomous Driving

Jin, Xianjian; Yang, Hang; He, Xiongkui; Li, Guohua; Yan, Zeyuan; Wang, Qikang

doi:10.3390/rs15123160

Cited by 11 publications

(6 citation statements)

References 33 publications

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“…Compared to methods based on visual data, there has been relatively less research conducted on LiDAR-and radar-based methods. Jin et al [23] proposed a robust vehicle detection approach based on LiDAR, consisting of three components: point cloud clustering, bounding box fitting, and point cloud recognition. To eliminate the need for manual feature engineering on 3D point clouds, Zhou et al [24] introduced VoxelNet, a universal 3D detection network that integrates feature extraction and bounding box prediction into a single-step, end-to-end trainable deep network.…”

Section: Materials 21 Single-modality Methodsmentioning

confidence: 99%

Deep LiDAR-Radar-Visual Fusion for Object Detection in Urban Environments

Xiao,

Liu,

Luan

et al. 2023

Remote Sensing

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Robust environmental sensing and accurate object detection are crucial in enabling autonomous driving in urban environments. To achieve this goal, autonomous mobile systems commonly integrate multiple sensor modalities onboard, aiming to enhance accuracy and robustness. In this article, we focus on achieving accurate 2D object detection in urban autonomous driving scenarios. Considering the occlusion issues of using a single sensor from a single viewpoint, as well as the limitations of current vision-based approaches in bad weather conditions, we propose a novel multi-modal sensor fusion network called LRVFNet. This network effectively combines data from LiDAR, mmWave radar, and visual sensors through a deep multi-scale attention-based architecture. LRVFNet comprises three modules: a backbone responsible for generating distinct features from various sensor modalities, a feature fusion module utilizing the attention mechanism to fuse multi-modal features, and a pyramid module for object reasoning at different scales. By effectively fusing complementary information from multi-modal sensory data, LRVFNet enhances accuracy and robustness in 2D object detection. Extensive evaluations have been conducted on the public VOD dataset and the Flow dataset. The experimental results demonstrate the superior performance of our proposed LRVFNet compared to state-of-the-art baseline methods.

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Section: Materials 21 Single-modality Methodsmentioning

confidence: 99%

Deep LiDAR-Radar-Visual Fusion for Object Detection in Urban Environments

Xiao,

Liu,

Luan

et al. 2023

Remote Sensing

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“…The k-d tree is a commonly used data structure for efficient point retrieval in k-dimensional space [29,30]. In this study, a k-d tree was constructed to accelerate the spatial indexing of point cloud and improve computational efficiency.…”

Section: K-d Tree Data Structurementioning

confidence: 99%

A Novel Cone Model Filtering Method for Outlier Rejection of Multibeam Bathymetric Point Cloud: Principles and Applications

Lv,

Wang,

Huang

et al. 2023

Sensors

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The utilization of multibeam sonar systems has significantly facilitated the acquisition of underwater bathymetric data. However, efficiently processing vast amounts of multibeam point cloud data remains a challenge, particularly in terms of rejecting massive outliers. This paper proposes a novel solution by implementing a cone model filtering method for multibeam bathymetric point cloud data filtering. Initially, statistical analysis is employed to remove large-scale outliers from the raw point cloud data in order to enhance its resistance to variance for subsequent processing. Subsequently, virtual grids and voxel down-sampling are introduced to determine the angles and vertices of the model within each grid. Finally, the point cloud data was inverted, and the custom parameters were redefined to facilitate bi-directional data filtering. Experimental results demonstrate that compared to the commonly used filtering method the proposed method in this paper effectively removes outliers while minimizing excessive filtering, with minimal differences in standard deviations from human-computer interactive filtering. Furthermore, it yields a 3.57% improvement in accuracy compared to the Combined Uncertainty and Bathymetry Estimator method. These findings suggest that the newly proposed method is comparatively more effective and stable, exhibiting great potential for mitigating excessive filtering in areas with complex terrain.

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“…Road obstacles are vehicles, pedestrians, roadblocks, scattered objects, and other static or dynamic obstacles around the road which may affect driving safety. Vision-based or vehicle-mounted LIDAR-based detection methods are often used to detect such obstacles [24,25]. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Study on Obstacle Detection Method Based on Point Cloud Registration

Wang,

Wang

et al. 2024

WEVJ

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An efficient obstacle detection system is one of the most important guarantees for improving the active safety performance of autonomous vehicles. This paper proposes an obstacle detection method based on high-precision positioning applied to blocked zones to solve the problems of the high complexity of detection results, low computational efficiency, and high load in traditional obstacle detection methods. Firstly, an NDT registration method which uses the likelihood function as the optimal value of the registration score function to calculate the registration parameters is designed to match the scanning point cloud and the target point cloud. Secondly, a target reduction method combined with threshold judgment and the binary tree search algorithm is designed to filter the point cloud of non-road obstacles to improve the processing speed of the computing platform. Meanwhile, KD-tree is used to speed up the clustering process. Finally, a vehicle remote control simulation platform with the combination of a cloud platform and mobile terminal is designed to verify the effectiveness of the strategy in practical application. The results prove that the proposed obstacle detection method can improve the efficiency and accuracy of detection.

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Robust LiDAR-Based Vehicle Detection for On-Road Autonomous Driving

Cited by 11 publications

References 33 publications

Deep LiDAR-Radar-Visual Fusion for Object Detection in Urban Environments

Deep LiDAR-Radar-Visual Fusion for Object Detection in Urban Environments

A Novel Cone Model Filtering Method for Outlier Rejection of Multibeam Bathymetric Point Cloud: Principles and Applications

Study on Obstacle Detection Method Based on Point Cloud Registration

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