Evidence map›Paper›PMID 35431782›Full record

ArticleFrontiers in neuroscience2022

Automotive Radar Processing With Spiking Neural Networks: Concepts and Challenges.

Bernhard Vogginger, Felix Kreutz, Javier López-Randulfe, Chen Liu, Robin Dietrich, Hector A Gonzalez, Daniel Scholz, Nico Reeb, Daniel Auge, Julian Hille and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Bernhard VoggingerChair of Highly-Parallel VLSI-Systems and Neuro-Microelectronics, Faculty of Electrical and Computer Engineering, Institute of Principles of Electrical and Electronic Engineering, Technische Universität Dresden, Dresden, Germany.
Felix KreutzChair of Highly-Parallel VLSI-Systems and Neuro-Microelectronics, Faculty of Electrical and Computer Engineering, Institute of Principles of Electrical and Electronic Engineering, Technische Universität Dresden, Dresden, Germany.
Javier López-RandulfeDepartment of Informatics, Technical University of Munich, Munich, Germany.
Chen LiuChair of Highly-Parallel VLSI-Systems and Neuro-Microelectronics, Faculty of Electrical and Computer Engineering, Institute of Principles of Electrical and Electronic Engineering, Technische Universität Dresden, Dresden, Germany.
Robin DietrichDepartment of Informatics, Technical University of Munich, Munich, Germany.
Hector A GonzalezChair of Highly-Parallel VLSI-Systems and Neuro-Microelectronics, Faculty of Electrical and Computer Engineering, Institute of Principles of Electrical and Electronic Engineering, Technische Universität Dresden, Dresden, Germany.
Daniel ScholzChair of Highly-Parallel VLSI-Systems and Neuro-Microelectronics, Faculty of Electrical and Computer Engineering, Institute of Principles of Electrical and Electronic Engineering, Technische Universität Dresden, Dresden, Germany.
Nico ReebDepartment of Informatics, Technical University of Munich, Munich, Germany.
Daniel AugeDepartment of Informatics, Technical University of Munich, Munich, Germany.
Julian HilleDepartment of Informatics, Technical University of Munich, Munich, Germany.
Muhammad ArsalanInfineon Technologies AG, Munich, Germany.
Florian MirusBMW Group, Research, New Technologies, Garching, Germany.
Cyprian GrassmannInfineon Technologies AG, Munich, Germany.
Alois KnollDepartment of Informatics, Technical University of Munich, Munich, Germany.
Christian MayrChair of Highly-Parallel VLSI-Systems and Neuro-Microelectronics, Faculty of Electrical and Computer Engineering, Institute of Principles of Electrical and Electronic Engineering, Technische Universität Dresden, Dresden, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Frequency-modulated continuous wave radar sensors play an essential role for assisted and autonomous driving as they are robust under all weather and light conditions. However, the rising number of transmitters and receivers for obtaining a higher angular resolution increases the cost for digital signal processing. One promising approach for energy-efficient signal processing is the usage of brain-inspired spiking neural networks (SNNs) implemented on neuromorphic hardware. In this article we perform a step-by-step analysis of automotive radar processing and argue how spiking neural networks could replace or complement the conventional processing. We provide SNN examples for two processing steps and evaluate their accuracy and computational efficiency. For radar target detection, an SNN with temporal coding is competitive to the conventional approach at a low compute overhead. Instead, our SNN for target classification achieves an accuracy close to a reference artificial neural network while requiring 200 times less operations. Finally, we discuss the specific requirements and challenges for SNN-based radar processing on neuromorphic hardware. This study proves the general applicability of SNNs for automotive radar processing and sustains the prospect of energy-efficient realizations in automated vehicles.

Indexed as

automotiveFMCWMIMOneuromorphic computingradar processingsignal processingspiking neural networks

Identifiers

PMID35431782
PMCPMC9012531

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.