Spike-Based Vision for Autonomous Driving Scenarios by Zhang Jiyuan, Zheng Yajing, Yu Zhaofei, Huang Tiejun

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A Method for Autonomous Driving Trajectory Planning in Parking Environments by LIN Chun, HE Yuesheng, FANG Xingqi, WANG Chunxiang

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A localization and navigation method for underground mine autonomous driving based on local geometric topology map by LIU Shijie, ZOU Yuan, ZHANG Xudong

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Research progress of intelligent network and analysis of warship application by WU Jianlu, MENG Guangya, LIU Yu

Subjects: “…intent-based network|intent-driven network|autonomous driving network|autonomous network|warship application…”
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Vehicle‒Infrastructure Cooperative Sensing: Progress and Prospect by Yi Xiaoying, Rui Yikang, Ran Bin, Luo Kaijie, Sun Hucheng

Subjects: “…autonomous driving…”
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Trajectory Prediction Method Based on Fusion of Graph Interaction and Scene Perception by FANG Yang, ZHAO Ting, LIU Qi-lie, HE Dong, SUN Kai-wei, CHEN Qian-bin

“…To accurately perceive the environment and predict the trajectory of the surrounding traffic participants for autonomous driving,we propose a real-time end-to-end trajectory prediction framework based on bird eye view(BEV) to learn both interaction and scene information simultaneously.The framework consists of two essential modules:graph interaction network and pyramid perception network.The former encodes the interaction patterns among traffic participants through a spatiotemporal graph convolutional network,and the latter adopts a spatiotemporal pyramid network to model the surrounding information and obtain the scene features.Next,interactive features and scene features are fused at a unified scale to perform classification and trajectory prediction tasks.Experiments and analysis on Nuscenes,a large open-source dataset,indicate that the proposed framework achieves a higher classification accuracy of 3.1% and 1.43% less predicted trajectory loss than MotionNet.Hence,our framework outperforms state-of-the-art algorithms in terms of generalization and robustness,and is more in line with perception requirements in actual autonomous driving scenes.…”
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Design of Monitoring Solution for IP Network Quality Optimization by Xu HUANG, Meng-hong CHENG, Zhi-yan CHENG

“…The proposed design can both improve the quality of services, and further enhance the existing network's " autonomous driving" capabilities.…”
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3D point cloud object detection algorithm based on Transformer by LIU Mingyang, YANG Qiming, HU Guanhua, GUO Yan, ZHANG Jiandong

“…This article proposes a Transformer based 3D point cloud object detection algorithm, and combines the characteristics of point clouds in autonomous driving scenarios to propose an improved spatial modulation attention and heat map initialization strategy for training acceleration and query initialization, achieving good detection performance in shallow networks. …”
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Survey of Rigid Object Pose Estimation Algorithms Based on Deep Learning by GUO Nan, LI Jingyuan, REN Xi

“…Rigid object pose estimation aims to obtain 3D translation and 3D rotation information of the rigid object in the camera coordinate system,which plays an important role in rapidly developing fields such as autonomous driving,robotics and augmented reality.The representative papers on rigid object pose estimation based on deep learning from 2017 to 2021 are summarized and analyzed.The rigid object pose estimation methods are divided into coordinate-based,keypoints-based and template-based me-thods.The rigid object pose estimation task is divided into four sub-tasks:image preprocessing,spatial mapping or feature ma-tching,pose recovery,and pose optimization.The subtask realization of each method and its advantages and problems are introduced in detail.The challenges of rigid object pose estimation are analyzed,and the existing solutions and their advantages and disadvantages are summarized.Based on the rigid object pose estimation method,the articulated object and deformable object pose estimation are analyzed.The common datasets and performance evaluation indexes of rigid object pose estimation are introduced,and the performance of existing methods on common datasets is compared and analyzed.Finally,the future research directions of pose tracking and class rigid object pose estimation are prospected.…”
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AFTM:Anchor-free Object Tracking Method with Attention Features by LI Xuehui, ZHANG Yongjun, SHI Dianxi, XU Huachi, SHI Yanyan

“…As an important branch in the field of computer vision,object tracking has been widely used in many fields such as intelligent video surveillance,human-computer interaction and autonomous driving.Although object tracking has achieved good development in recent years,tracking in complex environment is still a challenge.Due to problems such as occlusion,object deformation and illumination change,tracking performance will be inaccurate and unstable.In this paper,an effective object tracking method AFTM,is proposed with attention features.Firstly,this paper constructs an adaptively generated attention weight factor group,which implements an efficient adaptive fusion strategy for response map to improve the accuracy of object positioning and bounding box scale calculation in the process of classification and regression.Secondly,aiming at the class imbalance in the data set,the proposed method uses the dynamically scaled cross entropy loss as the loss function of the object positioning network,which can modify the optimization direction of the model and make the tracking performance more stable and reliable.Finally,this paper designs a corresponding learning rate adjustment strategy to stochastically average the weight of a number of models,which can enhance the generalization ability of the model.Experimental results on public data sets show that the proposed method has higher accuracy and more stable tracking performance in complex tracking environment.…”
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Survey of 3D Model Recognition Based on Deep Learning by ZHOU Yan, LI Wenjun, DANG Zhaolong, ZENG Fanzhi, YE Dewang

“…Since deep learning has already achieved significant success in two-dimensional visual tasks, its introduction into the realm of three-dimensional visual perception not only breaks free from the constraints of traditional methods but also makes notable strides in areas such as autonomous driving and intelligent robotics. However, the application of deep learning techniques to 3D model recognition tasks still faces several challenges. …”
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Survey on Image Panoptic Segmentation Based on Deep Learning by BI Yangyang, ZHENG Yuanfan, SHI Caijuan, ZHANG Kun, LIU Jian

“…Thirdly, the application of image panoptic segmentation in medicine, autonomous driving, drones, agriculture, animal husbandry, military and other fields are listed. …”
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Device-Cloud Collaborative Intelligent Computing: Key Problems, Methods, and Applications by Zhang Shengyu, Kuang Kun, Lyu Chengfei, Li Jiwei, Xiao Jun, Wu Fan, Wu Fei

“…The study also summarizes applications in vertical domains such as recommendation systems, autonomous driving, security systems, and  educational models. …”
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