Single Chip Radar Processing for Object Detection

Nagalikar, Shivasharan; Mody, Mihir; Baranwal, Ankur; Kumar, VC; Shankar, Prithvi; Farooqui, Mohammad Asif; Shah, Malav; Sangani, Nikhil; Rakesh, Yc; Karkisaval, Abhishek; Chellappan, Sriraj; Sm, Stalin; Pratti, Anita; Rosales, Randy; Dixit, Tanu; Murphy, Sean

doi:10.1109/icce56470.2023.10043549

Cited by 3 publications

(2 citation statements)

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“…The main advantage of the single-chip solution relies in the fact that, with the same system, it is possible to both drive and sample the analogue front-end, perform the detection tasks and communicate with the rest of the vehicle all with the same platform. Other noticeable implementations of single-chip radar processing systems that include hardware acceleration of computationally intensive tasks are presented in [81][82][83].…”

Section: Digital Hardware Architectures For On-vehicle Sensing Tasksmentioning

confidence: 99%

A Survey of Automotive Radar and Lidar Signal Processing and Architectures

Giuffrida,

Masera,

Martina

2023

Chips

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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: Digital Hardware Architectures For On-vehicle Sensing Tasksmentioning

confidence: 99%

A Survey of Automotive Radar and Lidar Signal Processing and Architectures

Giuffrida,

Masera,

Martina

2023

Chips

View full text Add to dashboard Cite

show abstract

“…In object detection [4,5,6,7], the neck network primarily enhances the extraction of features from the backbone network. It separately extracts low-level features from different layers of the backbone network to obtain higher-level features and fuses features of different levels and resolutions.…”

Section: Introductionmentioning

confidence: 99%