Recent evolution of automotive imaging radar and its information content

Brisken, Stefan; Ruf, Florian; Höhne, Felix

doi:10.1049/iet-rsn.2018.0026

Cited by 37 publications

(26 citation statements)

References 9 publications

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“…In the same reference [ 79 ], a brief review of the key developments in radar and signal processing techniques applied to the estimation of significant target parameters, such as range, velocity, and direction, are presented with mathematical illustrations. Thanks to extensive use in the automotive industry for various applications and due to advances in signal processing by applying machine learning, pattern recognition techniques, and robust algorithm developments [ 80 ], radar data now have more knowledge of object dimension [ 81 ], object orientation, motion prediction [ 82 ], and classification information [ 83 , 84 , 85 ].…”

Section: Sensorsmentioning

confidence: 99%

The Perception System of Intelligent Ground Vehicles in All Weather Conditions: A Systematic Literature Review

Mohammed

Amamou

Ayevide

et al. 2020

Sensors

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Perception is a vital part of driving. Every year, the loss in visibility due to snow, fog, and rain causes serious accidents worldwide. Therefore, it is important to be aware of the impact of weather conditions on perception performance while driving on highways and urban traffic in all weather conditions. The goal of this paper is to provide a survey of sensing technologies used to detect the surrounding environment and obstacles during driving maneuvers in different weather conditions. Firstly, some important historical milestones are presented. Secondly, the state-of-the-art automated driving applications (adaptive cruise control, pedestrian collision avoidance, etc.) are introduced with a focus on all-weather activity. Thirdly, the most involved sensor technologies (radar, lidar, ultrasonic, camera, and far-infrared) employed by automated driving applications are studied. Furthermore, the difference between the current and expected states of performance is determined by the use of spider charts. As a result, a fusion perspective is proposed that can fill gaps and increase the robustness of the perception system.

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Section: Sensorsmentioning

confidence: 99%

The Perception System of Intelligent Ground Vehicles in All Weather Conditions: A Systematic Literature Review

Mohammed

Amamou

Ayevide

et al. 2020

Sensors

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“…It is indisputable that cameras and LIDAR fail to varying degrees in the rain, snow and fog and under operating conditions such as bright light and darkness, while the MMW radar is indispensable as it shows strong robustness under bad conditions [ 4 ]. The traditional MMW radar for commercial vehicles is affected by its resolution, making it challenging to perform object classification tasks [ 5 ]. Furthermore, it lacks object height information and serves only as a last line of defense in most autonomous driving systems, acting as an advanced warning.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, it lacks object height information and serves only as a last line of defense in most autonomous driving systems, acting as an advanced warning. With the advent of a new generation of 4D high-resolution imaging radars [ 5 , 6 , 7 ], promising applications have been seen. The imaging radar can produce LIDAR-like point-cloud data, contain rich Doppler information and it has all-weather characteristics.…”

Section: Introductionmentioning

confidence: 99%

“…Michael et al [ 10 ] proposed a radar-centric autonomous vehicle dataset for 3D target detection based on radar, LiDAR and camera data. The dataset uses a Astyx 6455 HiRes [ 5 ] high-resolution imaging radar, which provides semi-automatically generated and manually refined 3D ground truth for object detection, with most of the objects being cars. Object classification datasets based on imaging radars are not yet available to the best of our knowledge; thus, object classification algorithms based on imaging radars need to be studied urgently.…”

Section: Introductionmentioning

confidence: 99%

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Radar Transformer: An Object Classification Network Based on 4D MMW Imaging Radar

Bai

Zheng

et al. 2021

Sensors

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Automotive millimeter-wave (MMW) radar is essential in autonomous vehicles due to its robustness in all weather conditions. Traditional commercial automotive radars are limited by their resolution, which makes the object classification task difficult. Thus, the concept of a new generation of four-dimensional (4D) imaging radar was proposed. It has high azimuth and elevation resolution and contains Doppler information to produce a high-quality point cloud. In this paper, we propose an object classification network named Radar Transformer. The algorithm takes the attention mechanism as the core and adopts the combination of vector attention and scalar attention to make full use of the spatial information, Doppler information, and reflection intensity information of the radar point cloud to realize the deep fusion of local attention features and global attention features. We generated an imaging radar classification dataset and completed manual annotation. The experimental results show that our proposed method achieved an overall classification accuracy of 94.9%, which is more suitable for processing radar point clouds than the popular deep learning frameworks and shows promising performance.

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“…Phased array radar systems are nowadays ubiquitous in a large number of applications requiring reliable and continuous sensing of the environment. Recent new applications include fields such as human vital sign detection and tracking [1], [2], autonomous driving [3], [4] and space surveillance [5], [6]. The detection and localization of targets of interest are the key functionalities of the radar system in each of these applications.…”

Section: Introductionmentioning

confidence: 99%

Dimension-Reduced Rx Beamforming Optimized for Simultaneous Detection and Estimation

Neuberger

Vehmas²,

Ender

2021

IEEE Trans. Aerosp. Electron. Syst.

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Phased array radar systems are indispensable in many applications requiring robust sensing of the environment. To achieve sensitive target detection and accurate direction-ofarrival (DOA) estimation, a high number of receiving antenna elements is needed. The high dimension of the element level data inevitably leads to a large computational burden for the digital signal processing. This problem can be overcome by transforming the element level data into a lower dimensional beamspace. In this paper, we present a novel parameter-controlled design method to construct this transformation. If the dimension reduction is not too drastic, it jointly achieves optimal detection and DOA estimation performance. Otherwise, it meets pre-defined performance criteria by exploiting an acceptable trade-off between detection and DOA estimation performance. We propose a general design tool, which is not limited to a specific array configuration. The design tool comprises a pre-calculated set of plots, providing the radar designer an overview of possible performance for a given scenario. We describe a straightforward method to construct the corresponding transformation. Numerical studies highlight the superiority of the proposed design method.

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Recent evolution of automotive imaging radar and its information content

Cited by 37 publications

References 9 publications

The Perception System of Intelligent Ground Vehicles in All Weather Conditions: A Systematic Literature Review

The Perception System of Intelligent Ground Vehicles in All Weather Conditions: A Systematic Literature Review

Radar Transformer: An Object Classification Network Based on 4D MMW Imaging Radar

Dimension-Reduced Rx Beamforming Optimized for Simultaneous Detection and Estimation

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