A Realistic Radar Ray Tracing Simulator for Large MIMO-Arrays in Automotive Environments

Schasler, Christian; Hoffmann, Marcel; Bräunig, Johanna; Ullmann, Ingrid; Ebelt, Randolf; Vossiek, Martin

doi:10.1109/jmw.2021.3104722

Cited by 35 publications

(28 citation statements)

References 36 publications

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“…Moreover, simulation can be used to generate the safety-critical long-tail scenarios [97]. Physics-based simulation methods, such as ray tracing [98,99], are widely applied to generate synthetic radar point clouds. Experiments [99] show that ray tracing can successfully model the multi-path propagation and separability issue of close objects.…”

Section: Synthetic Datamentioning

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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Section: Synthetic Datamentioning

confidence: 99%

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

Zhou

Liu

Zhao

et al. 2022

Sensors

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“…However, without the specific infrastructure, such as an echo chamber the size of a vehicle [ 66 ], or knowledge of characteristic parameters, such as the antenna radiation pattern [ 32 ], the modelling can only be based on compromises and must focus on a very specific domain or use case [ 67 ]. These compromises include simplifications and assumptions, for example: replacing of the real material description model by a probabilistic material model in [ 68 ]; assuming the radiation pattern of the antenna is known in [ 32 ]; replacing complex objects by multiple scattering centres in [ 69 ]; or treating all metallic surfaces as perfect conductors (PEC) [ 54 ], while considering all other materials as absorbing in [ 40 ]. …”

Section: Classification From the System Integrator’s Perspectivementioning

confidence: 99%

A Survey on Modelling of Automotive Radar Sensors for Virtual Test and Validation of Automated Driving

Magosi

Rosenberger

et al. 2022

Sensors

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Radar sensors were among the first perceptual sensors used for automated driving. Although several other technologies such as lidar, camera, and ultrasonic sensors are available, radar sensors have maintained and will continue to maintain their importance due to their reliability in adverse weather conditions. Virtual methods are being developed for verification and validation of automated driving functions to reduce the time and cost of testing. Due to the complexity of modelling high-frequency wave propagation and signal processing and perception algorithms, sensor models that seek a high degree of accuracy are challenging to simulate. Therefore, a variety of different modelling approaches have been presented in the last two decades. This paper comprehensively summarises the heterogeneous state of the art in radar sensor modelling. Instead of a technology-oriented classification as introduced in previous review articles, we present a classification of how these models can be used in vehicle development by using the V-model originating from software development. Sensor models are divided into operational, functional, technical, and individual models. The application and usability of these models along the development process are summarised in a comprehensive tabular overview, which is intended to support future research and development at the vehicle level and will be continuously updated.

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“…The virtual array of the real radar generated by the MIMO scheme is a uniform linear array (ULA) using a time division multiplexing (TDM) approach. For the sake of simplicity and runtime performance no multiplexing scheme was simulated for the virtual sensor, since in the simulation all channels are separated automatically, as described in [19]. All antennas are placed solely in azimuth direction and therefore no elevation information is available in this measurement setup.…”

Section: Conceptmentioning

confidence: 99%

“…As mentioned before, the radar data simulation is based on the implementation in [19]. For the simulation environment a static 3D mesh of a virtual city from the open source automotive driving simulator CARLA [20] was exported.…”

Section: A Environment Modelmentioning

confidence: 99%

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Deep Learning Based Image Enhancement for Automotive Radar Trained With an Advanced Virtual Sensor

Christian¹,

Hoffmann²,

Ullmann³

et al. 2022

IEEE Access

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This paper introduces a novel deep learning based concept for image enhancement and distortion suppression in automotive radar signal processing. The deep neural network (DNN) is trained solely on virtual data that is generated by an automotive MIMO radar ray tracing simulator. The simulator mimics the raw data that would be provided by a specific automotive MIMO radar for which the signal processing is envisaged. This virtual radar sensor, which has the same properties as the real radar, creates the DNN training input data. The unique feature of our approach is that an advanced virtual radar sensor is used to create the target values for the DNN training. The advanced virtual sensor is a simulated radar that works on the same basic principle as the real radar and its resembling virtual sensor, but is significantly upgraded in terms of performance. In our experiments the advanced virtual sensor, e.g., has a notably larger array, i.e. a much better lateral resolution than the real radar. In addition, our advanced virtual sensor is not affected by multi-path effects, clutter, noise, beamforming sidelobes and other typical automotive radar distortions. The paper shows that a DNN trained on this advanced virtual sensor training data can deliver outstanding automotive radar signal processing results not only on simulated data but also on field data. The presented real-world automotive radar measurements show that multi-path, clutter and noise are efficiently suppressed by this DNN and that it has a remarkable ability to sharpen images and suppress sidelobes. The results presented in the paper suggest that the novel advanced DNN training concept based on virtual sensors offers opportunities that also go far beyond the radar signal processing capabilities illustrated. Hence, the concept put forward here is an attractive option for placing DNN-based radar signal processing on a novel and easy-to-implement foundation.

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A Realistic Radar Ray Tracing Simulator for Large MIMO-Arrays in Automotive Environments

Cited by 35 publications

References 36 publications

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

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

A Survey on Modelling of Automotive Radar Sensors for Virtual Test and Validation of Automated Driving

Deep Learning Based Image Enhancement for Automotive Radar Trained With an Advanced Virtual Sensor

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