Yi Han's research while affiliated with Xi’an International Studies University and other places
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Publications (23)
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CNN-based methods have made great progress in single-image rain removal. Most recent methods improve performance by increasing the depth of the network. To fully extract local and global features while reducing inference time, we propose a top-to-down attribute-insensitive multiscale hourglass network for rain streak and raindrop removal. For the r...
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Object detection is one of the fundamental tasks in computer vision, holding immense significance in the realm of intelligent mobile scenes. This paper proposes a hybrid cross-feature interaction (HCFI) attention module for object detection in intelligent mobile scenes. Firstly, the paper introduces multiple kernel (MK) spatial pyramid pooling (SPP...
Currently, the demand for automobiles is increasing, and daily travel is increasingly reliant on cars. However, accompanying this trend are escalating traffic safety issues. Surveys indicate that most traffic accidents stem from driver errors, both intentional and unintentional. Consequently, within the framework of vehicular intelligence, intellig...
![Characteristics of each stage and major security threats [121].](publication/364282375/figure/tbl2/AS:11431281088834362@1665325879571/Characteristics-of-each-stage-and-major-security-threats-121_Q320.jpg)
With the development and progress of information technology, especially V2X technology, the research focus of intelligent vehicles gradually shifted from single-vehicle control to multi-vehicle control, and the cooperative control system of intelligent connected vehicles became an important topic of development. In order to track the research progr...
This article studies the control problem of autonomous vehicle path following with coordination of active front steering and differential steering. A hierarchical control scheme including upper layer and lower layer is proposed. In the upper layer controller, a linear quadratic regulator based on extended state observer is proposed to generate the...
Low-light image enhancement is a preprocessing work for many recognition and tracking tasks for autonomous driving at night. It needs to handle various factors simultaneously including uneven lighting, low contrast, and artifacts. We propose a novel end-to-end Retinex-based illumination attention low-light enhancement network. Specifically, our pro...
Environment perception is the premise for intelligent vehicles to drive safely and stably. Despite the rapid development of road detection technology based on visual images, it is still challenging to robustly identify road areas in visual images due to the influence of illumination changes and noise. In order to solve this problem, we introduce a...
With the wide application of deep learning in the field of computer vision, the technology of object detection continues to make breakthroughs, and the bounding box regression technology is closely related to the accuracy of object detection results. This study proposes an Absolute size IoU (AIoU) loss function for bounding box regression, which fu...
The antidisturbance control problem of autonomous vehicle path tracking considering lateral stability is studied in this paper. This paper proposes an improved active disturbance rejection control (IADRC) control method including an improved extended state observer (IESO) and an error compensator based on LQR, where a new continuous nonlinear funct...
Cybersecurity is one of the most important challenges in the intelligent connected vehicle system. Interconnected vehicles are vulnerable to different network security attacks, which endanger the safety of passengers. This review paper firstly analyses the reasons why the current vehicle network is vulnerable to network attack and summarizes the th...
![MV3D perceptual fusion framework [83]: the network takes the bird’s eye...](publication/354344959/figure/fig2/AS:1080247839662096@1634562563510/MV3D-perceptual-fusion-framework-83-the-network-takes-the-birds-eye-view-and-front_Q320.jpg)
Environmental perception technology is the basis and premise of intelligent vehicle decision control of intelligent vehicles, a crucial link of intelligent vehicles to realize intelligence, and also the basic guarantee of its safety and intelligence. The accuracy and robustness of the perception algorithm will directly affect or even determine the...
![Faster RCNN series algorithm framework diagram [44, 55, 56]. (a) RCNN....](publication/353667322/figure/fig3/AS:1080286565662721@1634571796009/Faster-RCNN-series-algorithm-framework-diagram-44-55-56-a-RCNN-b-Fast-RCNN-c_Q320.jpg)
![Faster RCNN series algorithm framework diagram [44, 55, 56]. (a) RCNN....](publication/353667322/figure/fig4/AS:1080286565666837@1634571796118/Faster-RCNN-series-algorithm-framework-diagram-44-55-56-a-RCNN-b-Fast-RCNN-c_Q320.jpg)
![Faster RCNN series algorithm framework diagram [44, 55, 56]. (a) RCNN....](publication/353667322/figure/fig5/AS:1080286565675028@1634571796159/Faster-RCNN-series-algorithm-framework-diagram-44-55-56-a-RCNN-b-Fast-RCNN-c_Q320.jpg)
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Pedestrian detection is a specific application of object detection. Compared with general object detection, it shows similarities and unique characteristics. In addition, it has important application value in the fields of intelligent driving and security monitoring. In recent years, with the rapid development of deep learning, pedestrian detection...
The research and application of lidar in the establishment of high-precision maps and path planning of intelligent driving vehicles are being developed as a technical support for intelligent driving technology. In this paper we mainly focus on the research and application of lidar real-time positioning and map building technology in intelligent dri...
This study presents an intelligent distribution framework based on edge computing and proposes navigation and obstacle avoidance algorithms for connected logistics vehicles (CLVs) on the basis of Trimble BD982 positioning sensor and tentacle algorithm (TA). An edge computing framework for the distribution of CLVs is established, and the functions o...
Vehicle-to-everything (V2X) communications can be applied in emergency material scheduling due to their performance in collecting and transmitting disaster-related data in real time. The urgency of disaster depots can be judged based on the disaster area video, and the scenario coefficient can be evaluated for building a fairness model. This paper...
Urban traffic accidents are on the rise worldwide. In order to reduce the incidence rate of dangerous traffic events on road, it is important to train drivers using appropriate traffic scenarios. The objective of this study is to develop categories of hazardous traffic events that can be used to construct simulated hazardous traffic scenarios. A su...
This paper proposes a crack recognition method based on high-resolution line array cameras and adaptive lifting algorithm. By defining the crack rate, this algorithm calculates the ratio of the crack area to the area of the entire collected image to characterize the damage extent of the current section. The algorithm first uses image preprocessing...
As illegal access control, privacy disclosure and other threats in vehicular ad-hoc networks become more prevalent, and the importance of safety and privacy protection in vehicular communications grows significantly. The current communication protocols in vehicular communications primarily guarantee infrangibility and unconditional anonymity to ach...
Citations
... Object detection aims to locate and classify objects within images. Significant advancements in deep learning have established a robust foundation for its application in diverse fields such as intelligent driving vehicles [21], medical healthcare [22], agricultural robots [23], and remote sensing [24][25][26]. Currently, object detection methods are mainly categorized into two-stage and one-stage methods. ...
... Intelligent connected vehicle (ICV) [3], the combination of the Internet of vehicles and intelligent vehicle, is an intelligent mobile space that integrates modern communication technology and network technology. ICV facilitates the sharing of perceived data among vehicles and traffic infrastructure through communication and cloud computing, allowing for more informed decision making [4]. At present, with the gradual maturity and improvement of the automatic driving industry chain, ICV has entered the stage of mass production. ...
... In the application, there are three essential steps in MPC, i.e., the model prediction, rolling optimization and feedback correction. The most obvious advantage of MPC is that it can add multiple constraints in the control process, since these constraints play an influential role in the planning and control of vehicle motion [11][12][13][14][15]. MPC solves an optimal control problem (OCP) to get a sequence of control commands over a finite receding horizon that optimizes a certain control metric (objective); then, the first portion of the resulting sequence is applied to the system. ...
... Randomly select the original image data for random flipping, local clipping, length and width scaling, color histogram equalization [40], and median filtering [41], adding Gaussian noise [42], salt and pepper noise, and other operations. According to the previous analysis, the light change factor greatly interferes with target recognition, so the light adaptability of some original data is enhanced by adjusting the contrast and color difference [43]. Increase the data set to 2000 under a series of data enhancement operations. ...
... Therefore, registration positioning based on high-precision point cloud maps and perception sensors is often applied to intelligent vehicles. The positioning method with LIDAR basically meets the positioning needs of intelligent vehicles and has the advantages of high accuracy and strong reliability [4,5]. ...
![[object Object]](https://i1.rgstatic.net/ii/profile.image/377417416888321-1466994732731_Q64/Joon-Huang-Chuah-2.jpg)
... A standard performance metric for object class segmentation issues is the IOU score. It mainly analyzes the overlap between the bounding box generated by the model and the ground truth box, improving the localization effect of the target box [27,28]. However, when the bounding box and ground truth box have no overlap, it cannot reflect the distance between the two boxes. ...
... It operates independently of model information, possesses inherent decoupling capabilities, and is straightforward to implement in engineering applications [17,18]. As a result, LADRC has demonstrated favorable outcomes in path-following control [19][20][21]. ...
... Several studies [104][105][106] have explored how AI tools can augment the capabilities and scope of intelligent and electric vehicles by enabling adaptive predictive functions. Additionally, cyber security concerns are gaining prominence, particularly as vehicles become increasingly connected to infrastructure like buildings [107,108]. ...
... Many techniques have been established for accurate object tracking and detection [5]. Traditional algorithms, such as pixel-based segmentation, blob-based segmentation, K-Nearest Neighbor, support vector machine, and AdaBoost classifiers provide commendable results in agriculture [6]. ...
... However, they are still collecting only Level 2 items such as autonomous mode, conventional mode, and vehicle level. Tese items are not enough to analyze the AV TA since accidents can be initiated by environmental problems (e.g., bad weather and road conditions), unexpected situations caused by other road users (e.g., pedestrians, bicycles, and E-scooters) [5], incorrect environmental perception information collected from sensors [6], and cyber attacks on AV [7]. ...
