Anomaly Detection in Radar Data Using PointNets

Griebel, Thomas; Authaler, Dominik; Horn, Markus; Henning, Matti; Buchholz, Michael; Dietmayer, Klaus

doi:10.1109/itsc48978.2021.9564730

Cited by 11 publications

(5 citation statements)

References 12 publications

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“…Similarly, a point-based solution is proposed for detecting ghost targets in RADAR-based perception [114]. In [115], a novel grouping algorithm utilising popular DNN-based feature extraction architecture on points set is also proposed for anomaly detection on RADAR-based detection.…”

Section: Other Tasksmentioning

confidence: 99%

Introspection of DNN-Based Perception Functions in Automated Driving Systems: State-of-the-Art and Open Research Challenges

Yatbaz,

Dianati,

Woodman

2024

IEEE Trans. Intell. Transport. Syst.

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Section: Other Tasksmentioning

confidence: 99%

Introspection of DNN-Based Perception Functions in Automated Driving Systems: State-of-the-Art and Open Research Challenges

Yatbaz,

Dianati,

Woodman

2024

IEEE Trans. Intell. Transport. Syst.

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“…Unlike previous approaches that target global anomaly detection and work across various sensor types, the solution presented in [131] focused specifically on radar sensor readings. Thus, the authors have addressed the problem of ghost targets (false targets), which can interfere with radar operations.…”

Section: Deep Learning-based Techniquesmentioning

confidence: 99%

Anomaly Detection in Connected and Autonomous Vehicles: A Survey, Analysis, and Research Challenges

Baccari,

Hadded,

Ghazzai

et al. 2024

IEEE Access

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In Intelligent Transportation Systems (ITS), ensuring road safety has paved the way for innovative advancements such as autonomous driving. These self-driving vehicles, with their variety of sensors, harness the potential to minimize human driving errors and enhance transportation efficiency via sophisticated AI modules. However, the reliability of these sensors remains challenging, especially as they can be vulnerable to anomalies resulting from adverse weather, technical issues, and cyber-attacks. Such inconsistencies can lead to imprecise or erroneous navigation decisions for autonomous vehicles that can result in fatal consequences, e.g., failure in recognizing obstacles. This survey delivers a comprehensive review of the latest research on solutions for detecting anomalies in sensor data. After laying the foundation on the workings of the connected and autonomous vehicles, we categorize anomaly detection methods into three groups: statistical, classical machine learning, and deep learning techniques. We provide a qualitative assessment of these methods to underline existing research limitations. We conclude by spotlighting key research questions to enhance the dependability of autonomous driving in forthcoming studies.

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“…Instead, they can be detected by geometric methods [196,198]. With a radar ghost dataset, it is also possible to train a neural network for ghost detection, such as PointNet-based methods [89] and PointNet++-based methods [197,199]. Because of the signal diffusion, the higher-order reflections can be safely ignored.…”

Section: Ghost Object Detectionmentioning

confidence: 99%

“…Thus, ghost objects usually occur in a ring-shaped region with a similar distance as the real target. Accordingly, Griebel et al [199] designed a ring grouping to replace the multi-scale grouping in PointNet++. The scene structure and relationship between detections are important cues to identify ghost objects.…”

Section: Ghost Object Detectionmentioning

confidence: 99%

Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges

Zhou

Liu

Zhao

et al. 2022

Sensors

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With recent developments, the performance of automotive radar has improved significantly. The next generation of 4D radar can achieve imaging capability in the form of high-resolution point clouds. In this context, we believe that the era of deep learning for radar perception has arrived. However, studies on radar deep learning are spread across different tasks, and a holistic overview is lacking. This review paper attempts to provide a big picture of the deep radar perception stack, including signal processing, datasets, labelling, data augmentation, and downstream tasks such as depth and velocity estimation, object detection, and sensor fusion. For these tasks, we focus on explaining how the network structure is adapted to radar domain knowledge. In particular, we summarise three overlooked challenges in deep radar perception, including multi-path effects, uncertainty problems, and adverse weather effects, and present some attempts to solve them.

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Anomaly Detection in Radar Data Using PointNets

Cited by 11 publications

References 12 publications

Introspection of DNN-Based Perception Functions in Automated Driving Systems: State-of-the-Art and Open Research Challenges

Introspection of DNN-Based Perception Functions in Automated Driving Systems: State-of-the-Art and Open Research Challenges

Anomaly Detection in Connected and Autonomous Vehicles: A Survey, Analysis, and Research Challenges

Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges

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