Research on vehicle intelligent wireless location algorithm based on convolutional neural network

Wang, Yazi; Feng, Yanghe; Sun, Huaibo

doi:10.1007/s00521-020-04911-w

Cited by 9 publications

(7 citation statements)

References 13 publications

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“…DenseNet [9] is a kind of dense connection model that has better expression ability compared with ResNet while maintaining a lower number of parameters. Under the premise of ensuring the timeliness of the tracking network, this paper uses DenseNet-121 as the feature extraction network [10] . The network design is shown in Table 1, which includes four Dense Block modules; every two modules are connected through the Transition Layer module.…”

Section: Re-identification Network Replacementmentioning

confidence: 99%

Intersection vehicle detection technology based on DeepSORT algorithm

Cui,

Zhang

2024

Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024)

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Section: Re-identification Network Replacementmentioning

confidence: 99%

Intersection vehicle detection technology based on DeepSORT algorithm

Cui,

Zhang

2024

Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024)

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“…Researchers have conducted simulation experiments to study the operating characteristics of unlicensed taxis. Wang et al [3] used a convolutional neural network to obtain trajectory data on unlicensed taxis and normal vehicles, performing feature learning and recognition to improve the recognition rate of unlicensed taxis. Shuai et al [4] proposed an unlicensed taxi identification algorithm based on k-medoids and utilizing radio frequency identification (RFID) data, which was authenticated through experiments.…”

Section: Introductionmentioning

confidence: 99%

Identification Method for Unlicensed Taxis in Urban Areas: A Case Study

Li¹,

Xiao²

2023

JUDM

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To accurately identify unlicensed taxis, this study measured their mileage using a traffic surveillance bayonet and obtained a threshold value by fitting a function to the mileage of previously identified unlicensed taxis. Abnormal driving vehicles were identified as those with a mileage exceeding the threshold value. Through a "white list" screening process, information on suspected unlicensed taxis was obtained. An empirical analysis of City A in Anhui Province showed an identification threshold of 85.8 km for unlicensed taxis. The study identified 68 highly suspected unlicensed taxis, 513 moderately suspected unlicensed taxis, and 1595 generally suspected unlicensed taxis. Suspected unlicensed taxis had a strong correlation with taxi mileage (r=0.895, sig(2-tailed)=0), with a mean mileage of 128.5 km and standard deviation of 50.8. This mileage was less than the average taxi mileage but significantly higher than the mileage traveled by private cars (mean=25.1 km, SD=16.4). The study's contribution lies in its development of a method for accurately identifying unlicensed taxis, which has significant implications for improving transportation safety.

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“…These contour information form a so-called point cloud and draw a 3D environment map with an accuracy of up to centimeter level, thus improving measurement accuracy. 22 Therefore, lidar technology can be applied to the research of key technologies for intelligent vehicle target recognition and tracking. This paper mainly studies the intelligent vehicle identification and tracking technology based on laser radar-based vehicle target recognition and tracking and obstacle detection based on grid map.…”

Section: Introductionmentioning

confidence: 99%

“…The laser beam can accurately measure the relative distance between the contour edge of the object in the field of view and the device. These contour information form a so-called point cloud and draw a 3D environment map with an accuracy of up to centimeter level, thus improving measurement accuracy 22 . Therefore, lidar technology can be applied to the research of key technologies for intelligent vehicle target recognition and tracking.…”

Section: Introductionmentioning

confidence: 99%

(Retracted) Laser radar-based intelligent vehicle target recognition and detection system using image detection technology

Zheng

2022

J. Electron. Imag.

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.In recent years, with the progress of society and the development of the economy, the number of cars in China has been increasing. In wireless communication networks, the choice of wireless nodes has a greater impact on the improvement of system performance (such as channel capacity, coverage area, etc.). The intelligent vehicle target detection system can perceive and recognize the surrounding objects such as pedestrians and vehicles through sensors, which is the basis for realizing the unmanned driving of intelligent vehicles. In a wireless environment where multiple wireless nodes coexist, current research focuses on how long it takes to re-plan the selection of wireless nodes (for mobile environments) and how to allocate and manage wireless nodes, such as where under the conditions of wireless, who will wireless with whom, and in what way (such as auto focus, digital fusion, or other) wireless, etc.; the dedicated centralized controller (such as the base station of the infrastructure-based wireless access network) determines the wireless partner (i.e., centralized type selection), or the wireless node decides by itself (i.e., distributed selection); select the appropriate number of wireless nodes to take into account the system performance gain and implementation complexity. The popularity of automobiles has brought great convenience to people’s lives, but it has also brought greater traffic pressure. At present, the amount of domestic automobile traffic has increased exponentially, and urban traffic congestion has become more serious, and even caused serious traffic accidents, directly affecting people’s quality of life. The emergence of intelligent transportation systems has effectively alleviated road traffic pressure and reduced the incidence of traffic accidents. As an important part of the intelligent transportation system, the research of intelligent vehicles has received extensive attention. Intelligent vehicles use a variety of sensors installed on the body to sense the environment, and realize intelligent driving functions such as lane line detection, obstacle detection, dynamic cruise control, and unmanned driving, which is conducive to reducing the incidence of traffic accidents and improving the safety of vehicle driving. At present, in the research of target recognition and tracking of intelligent vehicles, the traditional target detection method is mainly based on artificial feature extraction, which is difficult to describe more complex or higher-order image features, and the tracking effect is not good, which limits the target detection. An intelligent vehicle is an intelligent system with functions such as environment perception, planning decision-making, and operation control, and is an important part of the intelligent transportation system. Identify the effect. In response to these problems, we present a study of the target recognition and tracking of intelligent vehicles based on grid map and lidar sensor technology. To verify the proposed method in the actual scene, we identify ...

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Research on vehicle intelligent wireless location algorithm based on convolutional neural network

Cited by 9 publications

References 13 publications

Intersection vehicle detection technology based on DeepSORT algorithm

Intersection vehicle detection technology based on DeepSORT algorithm

Identification Method for Unlicensed Taxis in Urban Areas: A Case Study

(Retracted) Laser radar-based intelligent vehicle target recognition and detection system using image detection technology

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