A Neural Network Based System for Efficient Semantic Segmentation of Radar Point Clouds

Cennamo, Alessandro; Kaestner, Florian; Kummert, Anton

doi:10.1007/s11063-021-10544-4

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…The literature on radar-based segmentation, however, is still scarce. The work from [100] proposes the RadarPCNN model, based on the PointNet++ [83], to perform semantic segmentation on radar point clouds. [102] addresses the problem of open space segmentation for robot navigation, with focus in low-memory footprint and real-time processing.…”

Section: B Radarmentioning

confidence: 99%

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Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts, Datasets and Metrics

Manfio¹,

Osório²

2023

Preprint

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One of the main paths towards the reduction of traffic accidents is the increase in vehicle safety through driver assistance systems or even systems with a complete level of autonomy. In these types of systems, tasks such as obstacle detection and segmentation, especially the Deep Learning-based ones, play a fundamental role in scene understanding for correct and safe navigation. Besides that, the wide variety of sensors in vehicles nowadays provides a rich set of alternatives for improvement in the robustness of perception in challenging situations, such as navigation under lighting and weather adverse conditions. Despite the current focus given to the subject, the literature lacks studies on radar-based and radar-camera fusionbased perception. Hence, this work aims to carry out a study on the current scenario of camera and radar-based perception for ADAS and autonomous vehicles. Concepts and characteristics related to both sensors, as well as to their fusion, are presented. Additionally, we give an overview of the Deep Learning-based detection and segmentation tasks, and the main datasets, metrics, challenges, and open questions in vehicle perception.

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Section: B Radarmentioning

confidence: 99%

“…Its data can be represented as 2D maps and processed by Convolution Neural Networks for object detection [104], [105], segmentation [102], and classification [8]. Alternatively, radar data can also be represented as point clouds [84], [100].…”

Section: Sensor Fusionmentioning

confidence: 99%

Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts, Datasets and Metrics

Manfio¹,

Osório²

2023

Preprint

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Multi-Class Road User Detection With 3+1D Radar in the View-of-Delft Dataset

Palffy

Pool

Baratam

et al. 2022

IEEE Robot. Autom. Lett.

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Next-generation automotive radars provide elevation data in addition to range-, azimuth-and Doppler velocity. In this experimental study, we apply a state-of-the-art object detector (PointPillars), previously used for LiDAR 3D data, to such 3+1D radar data (where 1D refers to Doppler). In ablation studies, we first explore the benefits of the additional elevation information, together with that of Doppler, radar cross section and temporal accumulation, in the context of multi-class road user detection. We subsequently compare object detection performance on the radar and LiDAR point clouds, object class-wise and as a function of distance. To facilitate our experimental study, we present the novel View-of-Delft (VoD) automotive dataset. It contains 8693 frames of synchronized and calibrated 64-layer LiDAR-, (stereo) camera-, and 3+1D radar-data acquired in complex, urban traffic. It consists of 123106 3D bounding box annotations of both moving and static objects, including 26587 pedestrian, 10800 cyclist and 26949 car labels. Our results show that object detection on 64-layer LiDAR data still outperforms that on 3+1D radar data, but the addition of elevation information and integration of successive radar scans helps close the gap. The VoD dataset is made freely available for scientific benchmarking at https://intelligent-vehicles.org/datasets/view-ofdelft/.

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Review on Millimeter-Wave Radar and Camera Fusion Technology

Zhou¹,

Dong

Hou³

et al. 2022

Sustainability

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Cameras allow for highly accurate identification of targets. However, it is difficult to obtain spatial position and velocity information about a target by relying solely on images. The millimeter-wave radar (MMW radar) sensor itself easily acquires spatial position and velocity information of the target but cannot identify the shape of the target. MMW radar and camera, as two sensors with complementary strengths, have been heavily researched in intelligent transportation. This article examines and reviews domestic and international research techniques for the definition, process, and data correlation of MMW radar and camera fusion. This article describes the structure and hierarchy of MMW radar and camera fusion, it also presents its fusion process, including spatio-temporal alignment, sensor calibration, and data information correlation methods. The data fusion algorithms from MMW radar and camera are described separately from traditional fusion algorithms and deep learning based algorithms, and their advantages and disadvantages are briefly evaluated.

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A Neural Network Based System for Efficient Semantic Segmentation of Radar Point Clouds

Cited by 3 publications

References 25 publications

Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts, Datasets and Metrics

Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts, Datasets and Metrics

Multi-Class Road User Detection With 3+1D Radar in the View-of-Delft Dataset

Review on Millimeter-Wave Radar and Camera Fusion Technology

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