Spectranet: A High Resolution Imaging Radar Deep Neural Network for Autonomous Vehicles

Zheng, Ruxin; Sun, Shunqiao; Scharff, David E.; Wu, Teresa

doi:10.1109/sam53842.2022.9827798

Cited by 7 publications

(1 citation statement)

References 16 publications

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“…As presented by Kim et al [47], combining a Support Vector Machine (SVM) and a You Only Look Once (YOLO) model, it is possible to efficiently recognize and classify targets in radar points; in particular, they propose to identify the boundaries of the targets with the YOLO model and then use the SVM to classify them, obtaining an accuracy of 90%. Zheng et al [48] propose Spectranet as a possible deep-learning-based approach to moving object detection and classification with an accuracy of 81.9%.…”

Section: Signal Processing and Algorithms For Radarsmentioning

confidence: 99%

A Survey of Automotive Radar and Lidar Signal Processing and Architectures

Giuffrida,

Masera,

Martina

2023

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In recent years, the development of Advanced Driver-Assistance Systems (ADASs) is driving the need for more reliable and precise on-vehicle sensing. Radar and lidar are crucial in this framework, since they allow sensing of vehicle’s surroundings. In such a scenario, it is necessary to master these sensing systems, and knowing their similarities and differences is important. Due to ADAS’s intrinsic real-time performance requirements, it is almost mandatory to be aware of the processing algorithms required by radar and lidar to understand what can be optimized and what actions can be taken to approach the real-time requirement. This review aims to present state-of-the-art radar and lidar technology, mainly focusing on modulation schemes and imaging systems, highlighting their weaknesses and strengths. Then, an overview of the sensor data processing algorithms is provided, with some considerations on what type of algorithms can be accelerated in hardware, pointing to some implementations from the literature. In conclusion, the basic concepts of sensor fusion are presented, and a comparison between radar and lidar is performed.

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Section: Signal Processing and Algorithms For Radarsmentioning

confidence: 99%