A Camera–Radar Fusion Method Based on Edge Computing

Fu, Yanjin; Duan, Xunting; Zhou, Jianshan; Lang, Ping; Lin, Chunmian; You, Xiao‐Zeng

doi:10.1109/edge50951.2020.00009

Cited by 31 publications

(12 citation statements)

References 7 publications

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“…[35], a new method for calculating intersection phase time series based on interval detection data from millimeter-wave radars reduces vehicle congestion at intersections. And some systems based on the fusion of millimeter-wave radar and camera have been proposed to improve the robustness of vehicle information awareness on urban roads [36,37]. However, these studies based on the millimeter-wave radar to achieve traffic congestion relief are presented in simulation or approximate real simulation.…”

Section: Millimeter Wave Radar-based Road Application and Congestion ...mentioning

confidence: 99%

Urban traffic congestion alleviation system based on millimeter wave radar and improved probabilistic neural network

Yang

Zhang

et al. 2023

IET Radar Sonar & Navi

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The millimeter‐wave radar sensor is widely used for urban traffic surveillance because of its weather resistance and high detection accuracy. Methods such as fuzzy theory, pattern recognition, and artificial neural networks have been integrated into the research of traffic state discrimination. However, research on systematically describing the fusion of sensors and traffic state discrimination algorithms to alleviate urban road congestion is still lacking, especially based on millimeter‐wave radar. Thus, the authors propose an urban traffic congestion alleviation system framework. First, the design and deployment of the millimeter‐wave radar system, including waveforms, signal processing flow, and target tracking, are demonstrated to achieve vehicle information acquisition and output. Then, the appropriate traffic parameters are obtained by analysing traffic state influencing factors and the radar data characteristics. Finally, a traffic conditions identification algorithm combining spectral clustering and neural network algorithm is presented to realise road congestion level classification. The system is applied to real urban intersections rather than simulation or approximate real simulation. According to the current road congestion level, regulate the traffic light state to achieve road vehicle driving command. Experiments show that the proposed system can effectively reduce road congestion by 20% compared to the current fixed traffic light system.

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Section: Millimeter Wave Radar-based Road Application and Congestion ...mentioning

confidence: 99%

Urban traffic congestion alleviation system based on millimeter wave radar and improved probabilistic neural network

Yang

Zhang

et al. 2023

IET Radar Sonar & Navi

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show abstract

“…With the continuous deepening of research, some scholars have tried to deploy radar and camera perception equipment on the roadside to perceive the surrounding environment and provide more complete perception information for autonomous vehicles through wireless communication technology. Fu et al [ 47 ] utilized edge computing to localize the environmental perception data to effectively reduce latency. Meanwhile, the YOLOv3 [ 48 ] and DBSCAN clustering algorithms [ 49 ], often deployed in roadside equipment, are used to preprocess camera and radar data, respectively, to obtain information such as the position, speed, and category of targets.…”

Section: Cooperative Perception Information Fusionmentioning

confidence: 99%

Cooperative Perception Technology of Autonomous Driving in the Internet of Vehicles Environment: A Review

Cui

Zhang

Xiao

et al. 2022

Sensors

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Cooperative perception, as a critical technology of intelligent connected vehicles, aims to use wireless communication technology to interact and fuse environmental information obtained by edge nodes with local perception information, which can improve vehicle perception accuracy, reduce latency, and eliminate perception blind spots. It has become a current research hotspot. Based on the analysis of the related literature on the Internet of vehicles (IoV), this paper summarizes the multi-sensor information fusion method, information sharing strategy, and communication technology of autonomous driving cooperative perception technology in the IoV environment. Firstly, cooperative perception information fusion methods, such as image fusion, point cloud fusion, and image–point cloud fusion, are summarized and compared according to the approaches of sensor information fusion. Secondly, recent research on communication technology and the sharing strategies of cooperative perception technology is summarized and analyzed in detail. Simultaneously, combined with the practical application of V2X, the influence of network communication performance on cooperative perception is analyzed, considering factors such as latency, packet loss rate, and channel congestion, and the existing research methods are discussed. Finally, based on the summary and analysis of the above studies, future research issues on cooperative perception are proposed, and the development trend of cooperative perception technology is forecast to help researchers in this field quickly understand the research status, hotspots, and prospects of cooperative perception technology.

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“…However, the real positional detection was based on maize stems in this study, such leave reflected points made the calculated position deviate from the real position, too. Besides, although there did not exist visible positional changes between the camera and the Lidar, their positional variations were inevitable, some studies hold the viewpoint that vibration-caused positional changes between the camera and the Lidar is also a non-negligible reason for error occurrence [36] .…”

Section: Maize Seedling Detecting Accuracymentioning

confidence: 99%

Maize (Zea mays L.) seedling detection based on the fusion of a modified deep learning model and a novel Lidar points projecting strategy

Wang

Huang

Zhou

et al. 2022

International Journal of Agricultural and Biological Engineering

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Accurate crop detection is the prerequisite for the operation of intelligent agricultural machinery. Image recognition usually lacks accurate orientation information, and Lidar point clouds are not easy to distinguish different objects. Fortunately, the fusion of images and Lidar points can complement each other. This research aimed to detect maize (Zea mays L.) seedlings by fusing Lidar data with images. By applying coordinate transformation and time stamps, the images and Lidar points were realized homogeneous in spatial as well as temporal dimensions. Deep learning was used to develop a maize seedling recognition model, then the model recognized maize seedlings by labeling them with bounding boxes. Meanwhile, Lidar points were mapped to the bounding boxes. Only one-third of points that fell into the right middle of bounding boxes were selected for clustering operation, the calculated center of the cluster provided spatial information for target maize seedlings. This study modified the classical single shot multi-box detector (SSD) by merely linking the last feature map to the final output layer, owing to the higher feature maps having the unique advantages of detecting relatively larger objects. In images, maize seedlings were just the largest objects owing to be shot on purpose. This modification enabled the recognition model to finish recognizing an image by only consuming around 60 ms, which saved about 10 ms/image compared with the classical SSD model. The experiment was conducted in a maize field, and the maize was during the elongation stage. Experimental results demonstrated that the standard deviations for maximum distance error and maximum angle error were 1.4 cm and 1.1°, respectively, which can be tolerated under current technical requirements. Since agricultural fields are subject to staple crop-orientated and changeable ambient environment, the fusion of images and Lidar points can derive more precision information, and make agricultural machinery smarter. This study can act as an upstream technology for other researches on intelligent agricultural machinery.

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A Camera–Radar Fusion Method Based on Edge Computing

Cited by 31 publications

References 7 publications

Urban traffic congestion alleviation system based on millimeter wave radar and improved probabilistic neural network

Urban traffic congestion alleviation system based on millimeter wave radar and improved probabilistic neural network

Cooperative Perception Technology of Autonomous Driving in the Internet of Vehicles Environment: A Review

Maize (Zea mays L.) seedling detection based on the fusion of a modified deep learning model and a novel Lidar points projecting strategy

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