Interference Analysis for mmWave Automotive Radar Considering Blockage Effect [PDF]
Sensors, 2021 Due to the increasing number of vehicles equipped with millimeter wave (mmWave) radars, interference among automotive radars is becoming a major issue. This paper explores the automotive radar interference in both two-lane and multi-lane scenarios using ...
Liping Kui, Sai Huang, Zhiyong Feng
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Impact of system parameter selection on radar sensor performance in automotive applications [PDF]
Advances in Radio Science, 2012 The paper deals with the investigation of relevant boundary conditions to be considered in order to operate 77/79 GHz narrow and ultra wide band automotive radar sensors in the automotive platform and the automotive environment.
H.-L. Blöecher +5 more
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Automotive radar interference study for different radar waveform types
IET Radar, Sonar and Navigation, 2022 Mutual interference between different radar waveforms used in automotive radar applications is studied. The existing interference analysis is extended to a generalised radar‐to‐radar interference equation that covers most of the common interference ...
Utku Kumbul, Faruk Uysal
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Deep Learning Based Image Enhancement for Automotive Radar Trained With an Advanced Virtual Sensor
IEEE Access, 2022 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 ...
Marcel Hoffmann +2 more
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Mounting Angle Prediction for Automotive Radar Using Complex-Valued Convolutional Neural Network [PDF]
SensorsIn advanced driver-assistance systems (ADASs), the misalignment of the mounting angle of the automotive radar significantly affects the accuracy of object detection and tracking, impacting system safety and performance.
Sunghoon Moon, Younglok Kim
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A Machine Learning Perspective on Automotive Radar Direction of Arrival Estimation
IEEE Access, 2022 Millimeter-wave sensing using automotive radar imposes high requirements on the applied signal processing in order to obtain the necessary resolution for current imaging radar.
Jonas Fuchs +2 more
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YOLO-Based Simultaneous Target Detection and Classification in Automotive FMCW Radar Systems
Sensors, 2020 This paper proposes a method to simultaneously detect and classify objects by using a deep learning model, specifically you only look once (YOLO), with pre-processed automotive radar signals.
Hyun-Woong Cho +2 more
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A compact dual-band Dolly-shaped antenna with parasitic elements for automotive radar and 5G applications [PDF]
Heliyon, 2021 Ce Lakpo Bamy +2 more
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IEEE Access, 2021 Radar sensors are gradually becoming a wide-spread equipment for road vehicles, playing a crucial role in autonomous driving and road safety. The broad adoption of radar sensors increases the chance of interference among sensors from different vehicles ...
Nicolae-Catalin Ristea +2 more
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Continuous-time Radar-inertial Odometry for Automotive Radars [PDF]
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021 We present an approach for radar-inertial odometry which uses a continuous-time framework to fuse measurements from multiple automotive radars and an inertial measurement unit (IMU). Adverse weather conditions do not have a significant impact on the operating performance of radar sensors unlike that of camera and LiDAR sensors.
Ng, Yin Zhi +3 more
openaire +2 more sources