AI Article Synopsis
- The study focuses on the importance of millimeter-wave (MMW) radar in the development of automated vehicles (AVs) due to its cost-effectiveness, adaptability to weather conditions, and motion detection capabilities.
- The paper provides an overview of radar-based technologies, highlighting their use in various levels of autonomous driving, including their applications in advanced driving-assistance systems (ADAS) and high-level tasks like object detection and motion prediction.
- It also identifies gaps in existing research, particularly the lack of surveys on deep learning applications for radar data in AVs, and discusses future challenges and directions for further study.
Article Abstract
With the rapid development of automated vehicles (AVs), more and more demands are proposed towards environmental perception. Among the commonly used sensors, MMW radar plays an important role due to its low cost, adaptability In different weather, and motion detection capability. Radar can provide different data types to satisfy requirements for various levels of autonomous driving. The objective of this study is to present an overview of the state-of-the-art radar-based technologies applied In AVs. Although several published research papers focus on MMW Radars for intelligent vehicles, no general survey on deep learning applied In radar data for autonomous vehicles exists. Therefore, we try to provide related survey In this paper. First, we introduce models and representations from millimeter-wave (MMW) radar data. Secondly, we present radar-based applications used on AVs. For low-level automated driving, radar data have been widely used In advanced driving-assistance systems (ADAS). For high-level automated driving, radar data is used In object detection, object tracking, motion prediction, and self-localization. Finally, we discuss the remaining challenges and future development direction of related studies.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7766872 | PMC |
| http://dx.doi.org/10.3390/s20247283 | DOI Listing |
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