Abstract
The progress brought by the deep learning technology over the last decade has inspired many research domains, such as radar signal processing, speech and audio recognition, etc., to apply it to their respective problems. Most of the prominent deep learning models exploit data representations acquired with either Lidar or camera sensors, leaving automotive radars rarely used. This is despite the vital potential of radars in adverse weather conditions, as well as their ability to simultaneously measure an object’s range and radial velocity seamlessly. As radar signals have not been exploited very much so far, there is a lack of available benchmark data. However, recently, there has been a lot of interest in applying radar data as input to various deep learning algorithms, as more datasets are being provided. To this end, this paper presents a survey of various deep learning approaches processing radar signals to accomplish some significant tasks in an autonomous driving application, such as detection and classification. We have itemized the review based on different radar signal representations, as it is one of the critical aspects while using radar data with deep learning models. Furthermore, we give an extensive review of the recent deep learning-based multi-sensor fusion models exploiting radar signals and camera images for object detection tasks. We then provide a summary of the available datasets containing radar data. Finally, we discuss the gaps and important innovations in the reviewed papers and highlight some possible future research prospects.
Highlights
Over the last decade, autonomous driving and Advanced Driver Assistance Systems (ADAS) have been among the leading research domains explored in deep learning technology
Deep learning belongs to the subsets of machine learning algorithms that can be viewed as an extension of artificial neural networks (ANNs) applied to row sensory data to capture and extract high-level features that can be mapped to the target variable
A comprehensive review about radar data processing based on deep learning models is provided, covering different applications such as object classification, detection, and recognition
Summary
Autonomous driving and Advanced Driver Assistance Systems (ADAS) have been among the leading research domains explored in deep learning technology. Are adopting it to solve their respective problems, achieving promising results [20] In this respect, a lot of studies have been published over the recent years, pursuing multi-sensor fusion with various deep convolutional neural networks and obtaining a state-of-the-art performance in object detection and recognition [31,32,33,34,35]. A lot of studies have been published over the recent years, pursuing multi-sensor fusion with various deep convolutional neural networks and obtaining a state-of-the-art performance in object detection and recognition [31,32,33,34,35] The majority of these systems concentrate on multi-modal deep sensor fusion with cameras and Lidars as input to the neural network classifiers, neglecting automotive radars, primarily due to the relative availability of public accessible annotated datasets and benchmarks.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have