Adaptions for Automotive Radar Based Occupancy Gridmaps

Prophet, Robert; Stark, Henriette; Hoffmann, Marcel; Sturm, Christian; Vossiek, Martin

doi:10.1109/icmim.2018.8443484

Cited by 20 publications

(9 citation statements)

References 7 publications

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“…As the rapid development of point cloud techniques over past years, many works on free space detection based on it have been proposed [7,11,15,16,17]. Actually, free space detection based on point cloud consists of two tasks: data denoising and free space detection.…”

Section: Denoising Based Free Space Detectionmentioning

confidence: 99%

QDTree: Quasi-Density-Tree Accelerates Free Space Detection with MMW Radar Point Cloud

Li,

Yu,

et al. 2024

Advances in Transdisciplinary Engineering

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In autonomous driving, free space detection based on 3+1D radar point cloud severely suffers from the existence of noises. To tackle this problem, we propose the novel non-data-driven Quasi-Density-Tree (QDTree) method, where no distance metrics are employed, and within the QDTree we use voting tool to analyse its nodes in terms of point cloud density or SNR, resulting noise data effectively filtered. We further split left point clouds into static and moving types according to Doppler to detect free space and Gaussian Mixture Method was used to generate safety confidence. The QDTree structure and point cloud dichotomy allow us to apply parallel computation in free space detection. Furthermore, the proposed free space detection method outperforms those state-of-the-art algorithms in terms of computation speed and result accuracy. Specifically, the proposed free space detection method consumes less than 30ms (with parallel computation, it is within 18ms) when processing 5-frame accumulated 4-corner-radar point cloud (∼5000 points) and hence it is adequate to meet real-time requirements.

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Section: Denoising Based Free Space Detectionmentioning

confidence: 99%

QDTree: Quasi-Density-Tree Accelerates Free Space Detection with MMW Radar Point Cloud

Li,

Yu,

et al. 2024

Advances in Transdisciplinary Engineering

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“…Based on radar grid maps describing the static environment, free space can be further determined based on the border recognition algorithm [ 34 ]. Compared with LiDAR, occupied objects can be better detected, and a more accurate free space range can be obtained with radar due to its penetrability [ 33 ].…”

Section: Data Models and Representations From Mmw Radarmentioning

confidence: 99%

MMW Radar-Based Technologies in Autonomous Driving: A Review

Zhou

Yang

Jiang

et al. 2020

Sensors

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With the rapid development of automated vehicles (AVs), more and more demands are proposed towards environmental perception. Among the commonly used sensors, MMW radar plays an important role due to its low cost, adaptability In different weather, and motion detection capability. Radar can provide different data types to satisfy requirements for various levels of autonomous driving. The objective of this study is to present an overview of the state-of-the-art radar-based technologies applied In AVs. Although several published research papers focus on MMW Radars for intelligent vehicles, no general survey on deep learning applied In radar data for autonomous vehicles exists. Therefore, we try to provide related survey In this paper. First, we introduce models and representations from millimeter-wave (MMW) radar data. Secondly, we present radar-based applications used on AVs. For low-level automated driving, radar data have been widely used In advanced driving-assistance systems (ADAS). For high-level automated driving, radar data is used In object detection, object tracking, motion prediction, and self-localization. Finally, we discuss the remaining challenges and future development direction of related studies.

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“…To the best of our knowledge, this work is the first to utilize clustered radar data using a learnable model, which is an important use case for autonomous driving. Other works concentrate on raw radar data [9], [18], [25], [26], [6] or object level data for high level fusion [10], [4].…”

Section: Related Workmentioning

confidence: 99%

“…In addition to identifying occupied and free cells, the inability to observe a known state of cells is also an important concept in occupancy grid mapping and has been addressed in several ways. In cases where each cell is associated with an occupancy probability, such as in [25], [6], [18], a probability of 0.5 represents the highest uncertainty between occupied and free, and is equivalent to having no knowledge of a cell's occupancy state. Unobserved state can also be leveraged in inverse sensor modeling [20].…”

Section: Related Workmentioning

confidence: 99%

“…Among the classical methods, a delta function ISM is typically used for lidar occupancy grid generation [9], [23], [20], [11]. Since radar data is noisier, it is common to see a Gaussian variant (in range and azimuth) of the delta function ISM [9], [18], [25]. Algorithms operating on raw radar data are often required to clear noise and clutter, assess the detection probability / plausibility [13], [25], and perform clustering on the results before generating the occupancy grid mapping [13].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Road Scene Understanding by Occupancy Grid Learning from Sparse Radar Clusters using Semantic Segmentation

Sless

Shlomo

Cohen

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

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Occupancy grid mapping is an important component in road scene understanding for autonomous driving. It encapsulates information of the drivable area, road obstacles and enables safe autonomous driving. Radars are an emerging sensor in autonomous vehicle vision, becoming more widely used due to their long range sensing, low cost, and robustness to severe weather conditions. Despite recent advances in deep learning technology, occupancy grid mapping from radar data is still mostly done using classical filtering approaches. In this work, we propose learning the inverse sensor model used for occupancy grid mapping from clustered radar data. This is done in a data driven approach that leverages computer vision techniques. This task is very challenging due to data sparsity and noise characteristics of the radar sensor. The problem is formulated as a semantic segmentation task and we show how it can be learned using lidar data for generating ground truth. We show both qualitatively and quantitatively that our learned occupancy net outperforms classic methods by a large margin using the recently released NuScenes real-world driving data.

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Adaptions for Automotive Radar Based Occupancy Gridmaps

Cited by 20 publications

References 7 publications

QDTree: Quasi-Density-Tree Accelerates Free Space Detection with MMW Radar Point Cloud

QDTree: Quasi-Density-Tree Accelerates Free Space Detection with MMW Radar Point Cloud

MMW Radar-Based Technologies in Autonomous Driving: A Review

Road Scene Understanding by Occupancy Grid Learning from Sparse Radar Clusters using Semantic Segmentation

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