Automotive Radar Sub-Sampling via Object Detection Networks: Leveraging Prior Signal Information
In recent years, automotive radar has attracted considerable attention due to the growing interest in autonomous driving technologies. Acquiring situational awareness using multimodal data collected at high sampling rates by various sensing devices including cameras, LiDAR, and radar requires consid...
Main Authors: | Madhumitha Sakthi, Marius Arvinte, Haris Vikalo |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2023-01-01
|
Series: | IEEE Open Journal of Intelligent Transportation Systems |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10315142/ |
Similar Items
-
YOLO-Based Simultaneous Target Detection and Classification in Automotive FMCW Radar Systems
by: Woosuk Kim, et al.
Published: (2020-05-01) -
An Overview of Object Detection and Tracking Algorithms
by: Kehao Du, et al.
Published: (2023-06-01) -
Mobile Eye-Tracking Data Analysis Using Object Detection via YOLO v4
by: Niharika Kumari, et al.
Published: (2021-11-01) -
A Novel Method for Improving Point Cloud Accuracy in Automotive Radar Object Recognition
by: Guowei Lu, et al.
Published: (2023-01-01) -
A Smartphone-Based Application for Scale Pest Detection Using Multiple-Object Detection Methods
by: Jian-Wen Chen, et al.
Published: (2021-02-01)