Semantic radar grids

Lombacher, Jakob; Laudt, Kilian; Hahn, Markus; Dickmann, Jürgen; Wöhler, Christian

doi:10.1109/ivs.2017.7995871

Cited by 53 publications

(29 citation statements)

References 10 publications

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“…Early generations of driver assistance systems typically used a single environment perception sensor for each driver assistance function. Today's higher levels of automation often deploy sensor fusion concepts or semantic grids to detect also stationary objects [4]. The role of radar for driving automation has become increasingly important; with further developments towards higher resolution [5] and fully polarimetric devices [6], radar is considered a key sensor for autonomous driving [7].…”

Section: Radar Sensors In Autonomous Drivingmentioning

confidence: 99%

Measurements revealing Challenges in Radar Sensor Modeling for Virtual Validation of Autonomous Driving

Holder

Rosenberger

Winner

et al. 2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

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The virtual validation of automated driving functions requires meaningful simulation models of environment perception sensors such as radar, lidar, and cameras. There does not yet exist an unrivaled standard for perception sensor models, and radar especially lacks modeling approaches that consistently produce realistic results. In this paper, we present measurements that exemplify challenges in the development of meaningful radar sensor models. We highlight three major challenges: multi-path propagation, separability, and sensitivity of radar cross section to the aspect angle. We also review previous work addressing these challenges and suggest further research directions towards meaningful automotive radar simulation models.

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Section: Radar Sensors In Autonomous Drivingmentioning

confidence: 99%

Measurements revealing Challenges in Radar Sensor Modeling for Virtual Validation of Autonomous Driving

Holder

Rosenberger

Winner

et al. 2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

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“…The Radar data can be represented by 2D feature maps and processed by convolutional neural networks. For example, Lombacher et al employ Radar grid maps made by accumulating Radar data over several time-stamps [151] for static object classification [152] and semantic segmentation [153] in autonomous driving. Visentin et al show that CNNs can be employed for object classification in a post-processed range-velocity map [154].…”

Section: ) Radar Signalsmentioning

confidence: 99%

Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges

Feng

Haase-Schütz

Rosenbaum

et al. 2021

IEEE Trans. Intell. Transport. Syst.

808

338

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Recent advancements in perception for autonomous driving are driven by deep learning. In order to achieve robust and accurate scene understanding, autonomous vehicles are usually equipped with different sensors (e.g. cameras, LiDARs, Radars), and multiple sensing modalities can be fused to exploit their complementary properties. In this context, many methods have been proposed for deep multi-modal perception problems. However, there is no general guideline for network architecture design, and questions of "what to fuse", "when to fuse", and "how to fuse" remain open. This review paper attempts to systematically summarize methodologies and discuss challenges for deep multi-modal object detection and semantic segmentation in autonomous driving. To this end, we first provide an overview of on-board sensors on test vehicles, open datasets, and background information for object detection and semantic segmentation in autonomous driving research. We then summarize the fusion methodologies and discuss challenges and open questions. In the appendix, we provide tables that summarize topics and methods. We also provide an interactive online platform to navigate each reference: https://boschresearch.github.io/multimodalperception/. 0.99 0.8 0.98 0.99 0.96 0.96 0.94 Vehicle Person Road sign Traffic light LiDAR Points Map Radar Points RGB Image

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“…Then, windows around potential objects are cut out and used as input for a deep neural network. Furthermore, Lombacher et al [10] infer a semantic representation for stationary objects using radar data. To this end, a convolutional neural network is fed with an occupancy radar grid map.…”

Section: Related Workmentioning

confidence: 99%

2D Car Detection in Radar Data with PointNets

Danzer

Griebel

Bach

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

128

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For many automated driving functions, a highly accurate perception of the vehicle environment is a crucial prerequisite. Modern high-resolution radar sensors generate multiple radar targets per object, which makes these sensors particularly suitable for the 2D object detection task. This work presents an approach to detect 2D objects solely depending on sparse radar data using PointNets. In literature, only methods are presented so far which perform either object classification or bounding box estimation for objects. In contrast, this method facilitates a classification together with a bounding box estimation of objects using a single radar sensor. To this end, PointNets are adjusted for radar data performing 2D object classification with segmentation, and 2D bounding box regression in order to estimate an amodal 2D bounding box. The algorithm is evaluated using an automatically created dataset which consist of various realistic driving maneuvers. The results show the great potential of object detection in high-resolution radar data using PointNets.

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Semantic radar grids

Cited by 53 publications

References 10 publications

Measurements revealing Challenges in Radar Sensor Modeling for Virtual Validation of Autonomous Driving

Measurements revealing Challenges in Radar Sensor Modeling for Virtual Validation of Autonomous Driving

Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges

2D Car Detection in Radar Data with PointNets

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