System and parametric tradeoffs of forward looking automotive radar systems

2002 ◽  
Author(s):  
W. Nagy ◽  
J. Wilhelm
Keyword(s):  
Automotive Radar ◽  
Radar Systems ◽  
Forward Looking

Automotive Radars

2017 ◽  
Author(s):  
Sujeet Patole ◽  
Murat Torlak ◽  
Dan Wang ◽  
Murtaza Ali
Keyword(s):  
Signal Processing ◽  
Pedestrian Detection ◽  
Radar Signal ◽  
Automotive Radar ◽  
Estimation Algorithms ◽  
Radar Systems ◽  
Starting Point ◽  
Processing Techniques ◽  
Automotive Radars ◽  
Signal Processing Techniques

Automotive radars, along with other sensors such as lidar, (which stands for “light detection and ranging”), ultrasound, and cameras, form the backbone of self-driving cars and advanced driver assistant systems (ADASs). These technological advancements are enabled by extremely complex systems with a long signal processing path from radars/sensors to the controller. Automotive radar systems are responsible for the detection of objects and obstacles, their position, and speed relative to the vehicle. The development of signal processing techniques along with progress in the millimeter- wave (mm-wave) semiconductor technology plays a key role in automotive radar systems. Various signal processing techniques have been developed to provide better resolution and estimation performance in all measurement dimensions: range, azimuth-elevation angles, and velocity of the targets surrounding the vehicles. This article summarizes various aspects of automotive radar signal processing techniques, including waveform design, possible radar architectures, estimation algorithms, implementation complexity-resolution trade-off, and adaptive processing for complex environments, as well as unique problems associated with automotive radars such as pedestrian detection. We believe that this review article will combine the several contributions scattered in the literature to serve as a primary starting point to new researchers and to give a bird’s-eye view to the existing research community.

Pedestrian Classification for 79 GHz Automotive Radar Systems

2018 ◽  
Author(s):  
Robert Prophet ◽  
Marcel Hoffmann ◽  
Alicja Ossowska ◽  
Waqas Malik ◽  
Christian Sturm ◽  
...  
Keyword(s):  
Automotive Radar ◽  
Radar Systems

scholarly journals From Antenna Design to High Fidelity, Full Physics Automotive Radar Sensor Corner Case Simulation

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Ushemadzoro Chipengo ◽  
Peter M. Krenz ◽  
Shawn Carpenter
Keyword(s):  
Target Identification ◽  
Pedestrian Detection ◽  
High Fidelity ◽  
Driver Assistance ◽  
Automotive Radar ◽  
Driver Assistance Systems ◽  
Advanced Driver Assistance Systems ◽  
Radar Systems ◽  
False Target ◽  
Assistance Systems

Advanced driver assistance systems (ADAS) have recently been thrust into the spotlight in the automotive industry as carmakers and technology companies pursue effective active safety systems and fully autonomous vehicles. Various sensors such as lidar (light detection and ranging), radar (radio detection and ranging), ultrasonic, and optical cameras are employed to provide situational awareness to vehicles in a highly dynamic environment. Radar has emerged as a primary sensor technology for both active/passive safety and comfort-advanced driver-assistance systems. Physically building and testing radar systems to demonstrate reliability is an expensive and time-consuming process. Simulation emerges as the most practical solution to designing and testing radar systems. This paper provides a complete, full physics simulation workflow for automotive radar using finite element method and asymptotic ray tracing electromagnetic solvers. The design and optimization of both transmitter and receiver antennas is presented. Antenna interaction with vehicle bumper and fascia is also investigated. A full physics-based radar scene corner case is modelled to obtain high-fidelity range-Doppler maps. Finally, this paper investigates the effects of inclined roads on late pedestrian detection and the effects of construction metal plate radar returns on false target identification. Possible solutions are suggested and validated. Results from this study show how pedestrian radar returns can be increased by over 16 dB for early detection along with a 27 dB reduction in road construction plate radar returns to suppress false target identification. Both solutions to the above corner cases can potentially save pedestrian lives and prevent future accidents.

scholarly journals Enhanced DOA Estimation Using Linearly Predicted Array Expansion for Automotive Radar Systems

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 47714-47727 ◽  
Author(s):  
Heonkyo Sim ◽  
Seongwook Lee ◽  
Seokhyun Kang ◽  
Seong-Cheol Kim
Keyword(s):  
Doa Estimation ◽  
Automotive Radar ◽  
Radar Systems

scholarly journals YOLO-Based Simultaneous Target Detection and Classification in Automotive FMCW Radar Systems

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2897 ◽  
Author(s):  
Woosuk Kim ◽  
Hyunwoong Cho ◽  
Jongseok Kim ◽  
Byungkwan Kim ◽  
Seongwook Lee
Keyword(s):  
Spatial Clustering ◽  
Radar Data ◽  
Support Vector ◽  
Automotive Radar ◽  
Radar Sensor ◽  
Fmcw Radar ◽  
Radar Systems ◽  
Conventional Methods ◽  
Two Stages ◽  
Deep Learning Model

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. In conventional methods, the detection and classification in automotive radar systems are conducted in two successive stages; however, in the proposed method, the two stages are combined into one. To verify the effectiveness of the proposed method, we applied it to the actual radar data measured using our automotive radar sensor. According to the results, our proposed method can simultaneously detect targets and classify them with over 90% accuracy. In addition, it shows better performance in terms of detection and classification, compared with conventional methods such as density-based spatial clustering of applications with noise or the support vector machine. Moreover, the proposed method especially exhibits better performance when detecting and classifying a vehicle with a long body.

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