MmWave Radar Point Cloud Segmentation using GMM in Multimodal Traffic Monitoring

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.

Section: Deep Learning-based Multi-sensor Fusion Of Radar and Camementioning

confidence: 99%

Section: Deep Learning-based Multi-sensor Fusion Of Radar and Camementioning

confidence: 99%

Application of Deep Learning on Millimeter-Wave Radar Signals: A Review

Abdu

Zhang

et al. 2021

“…This leads to wrong coordinate estimation [ 12 ], together with the appearance of flying pixels and false surfaces [ 26 ]. Such impairments can also be found on other sensing devices such as Frequency-Modulated Continuous Wave (FMCW) radar [ 25 ], where environmental factors can deeply affect the quality of the resulting acquisition and the final processing performance [ 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

Recent Advancements in Learning Algorithms for Point Clouds: An Updated Overview

Camuffo

Mari

Milani

2022

Recent advancements in self-driving cars, robotics, and remote sensing have widened the range of applications for 3D Point Cloud (PC) data. This data format poses several new issues concerning noise levels, sparsity, and required storage space; as a result, many recent works address PC problems using Deep Learning (DL) solutions thanks to their capability to automatically extract features and achieve high performances. Such evolution has also changed the structure of processing chains and posed new problems to both academic and industrial researchers. The aim of this paper is to provide a comprehensive overview of the latest state-of-the-art DL approaches for the most crucial PC processing operations, i.e., semantic scene understanding, compression, and completion. With respect to the existing reviews, the work proposes a new taxonomical classification of the approaches, taking into account the characteristics of the acquisition set up, the peculiarities of the acquired PC data, the presence of side information (depending on the adopted dataset), the data formatting, and the characteristics of the DL architectures. This organization allows one to better comprehend some final performance comparisons on common test sets and cast a light on the future research trends.

“…In order to estimate the direction of arrival, examples were executed by combining them. In a related task based on imaging radar, Feng et al [ 8 ] used a high-resolution MMW radar sensor to obtain a radar point-cloud representation for traffic surveillance scenes. Based on a new feature vector, it used a multivariate Gaussian mixture model (GMM) for radar point cloud segmentation in an unsupervised learning environment, i.e., “point-by-point” classification.…”

Section: Introductionmentioning

confidence: 99%

Radar Transformer: An Object Classification Network Based on 4D MMW Imaging Radar

Bai

Zheng

et al. 2021

Automotive millimeter-wave (MMW) radar is essential in autonomous vehicles due to its robustness in all weather conditions. Traditional commercial automotive radars are limited by their resolution, which makes the object classification task difficult. Thus, the concept of a new generation of four-dimensional (4D) imaging radar was proposed. It has high azimuth and elevation resolution and contains Doppler information to produce a high-quality point cloud. In this paper, we propose an object classification network named Radar Transformer. The algorithm takes the attention mechanism as the core and adopts the combination of vector attention and scalar attention to make full use of the spatial information, Doppler information, and reflection intensity information of the radar point cloud to realize the deep fusion of local attention features and global attention features. We generated an imaging radar classification dataset and completed manual annotation. The experimental results show that our proposed method achieved an overall classification accuracy of 94.9%, which is more suitable for processing radar point clouds than the popular deep learning frameworks and shows promising performance.