"Automotive radar the key technology for autonomous driving: From detection and ranging to environmental understanding"

Dickmann, Juergen; Klappstein, Jens; Hahn, Markus; Appenrodt, Nils; Bloecher, Hans-Ludwig; Werber, Klaudius; Sailer, Alfons

doi:10.1109/radar.2016.7485214

Cited by 176 publications

(85 citation statements)

References 24 publications

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“…Evaluation metric: We qualitatively compare between the different methods using the IoU metric as shown in equation (6). The IoU is computed per each of the 3 classes (free, occupied and unobserved).…”

Section: Baseline Methodsmentioning

confidence: 99%

“…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%

“…Autonomous vehicles are required to operate in complex environments. To this end, occupancy grid mapping from various sensors is an important component of autonomous vehicle perception [25], [16], [15], [13], [6], [26]. In real world scenarios, training class specific detectors for all possible object classes a vehicle might encounter is intractable.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Baseline Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…To enable fully autonomous driving, high-resolution radar sensors are required [1]. For a linear frequency modulated continuous wave modulation, also called chirp-sequence, this means a bandwidth larger than 1 GHz [2], and several consecutive frequency ramps are evaluated by different receiving antenna elements.…”

Section: Introductionmentioning

confidence: 99%

Data rate reduction for chirp-sequence based automotive radars using compressed sensing

Roos¹,

Hügler²,

Knill³

et al. 2018

2018 11th German Microwave Conference (GeMiC)

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Abstract-For autonomous driving high-resolution radar sensors are key components, which have the drawback of high data rates. In order to reduce the amount of sampled data, random samples can be omitted and afterwards reconstructed using compressed sensing methods. A possible application is that not every receiving antenna element demands its own analog-todigital converter. One converter can be used for several receiving elements with a random assignment to each antenna instead. In this paper, an analysis is presented of how many samples can be neglected such that a successful reconstruction in post-processing for an automotive scenario is possible. A measurement result is shown to prove that with only 40 % of samples a successful reconstruction is possible.

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“…Above all, this concerns basic CW, frequency-modulated CW (FM-CW), step-frequency CW (SF-CW), or pseudo-noise CW (PN-CW) devices. Their popularity is due to their relative simplicity and the capability of being used as small sensors in many applications (automotive [1][2][3][4], medical [5], [6], human detection and rescue [7], [8], others [9]). Nowadays, probably the most important field is the automobile industry where FM-CW units are widely used as low-to mid-range radar sensors.…”

Section: Introductionmentioning

confidence: 99%

Extended Noise Analysis Model of CW-type Radar Sensors with IQ Down-Conversion

Hudec¹,

Jeník²,

Panek³

2018

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"Automotive radar the key technology for autonomous driving: From detection and ranging to environmental understanding"

Cited by 176 publications

References 24 publications

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

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

Data rate reduction for chirp-sequence based automotive radars using compressed sensing

Extended Noise Analysis Model of CW-type Radar Sensors with IQ Down-Conversion

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