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纸质出版日期： 2022-06-16 ，
录用日期： 2022-03-16
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曹行健, 张志涛, 孙彦赞, 王平, 徐树公, 刘富强, 王超, 彭飞, 穆世义, 刘文予, 杨铀. 面向智慧交通的图像处理与边缘计算[J]. 中国图象图形学报, 2022,27(6):1743-1767.
Xingjian Cao, Zhitao Zhang, Yanzan Sun, Ping Wang, Shugong Xu, Fuqiang Liu, Chao Wang, Fei Peng, Shiyi Mu, Wenyu Liu, You Yang. The review of image processing and edge computing for intelligent transportation system[J]. Journal of Image and Graphics, 2022,27(6):1743-1767.
随着全球人口的持续增长和城市化进程的加速，道路拥挤、交通事故和污染排放增加等问题日益严重。智慧交通系统旨在借助先进的信息与通信技术建成高效安全、环保舒适的交通与运输体系，提供全方位的交通信息服务和安全高效、经济快捷的交通运输与出行服务。经过各国多年来的竭力推进与发展，智慧交通系统在交通管理、自动驾驶与车路协同等方向均得到广泛的应用。智慧交通的发展离不开通信、计算机与控制等研究方向的突破与创新。其中，图像处理作为智慧交通系统的核心技术之一，它的研究进展直接影响着智慧交通系统的部署。图像处理技术是指计算机对图像进行增强、复原、提取特征、分类和分割等技术处理，通过对交通视觉图像的处理，为智慧交通系统的感知、识别、检测、跟踪和路径规划等功能提供了最直接与重要的信息。此外，面对智慧交通系统所产生的大量数据计算任务，边缘计算技术则将中心云服务下沉至各边缘节点附近，不但能够优化算力负载分配，还能够满足智慧交通应用与服务对低时延、高响应速度的需求。本文从智慧交通系统的发展现状入手，分别围绕面向智慧交通的图像处理与边缘计算技术，阐述其研究热点与前沿进展，汇总与比较国内外的相关学术和产业成果，并对智慧交通系统中的图像处理及边缘计算技术未来的发展进行总结分析与趋势展望。
Current intelligent transportation system (ITS) issue is challenged to improve conventional transportation engineering solutions nowadays. ITS has integrated advanced information and communication technologies (ICTs) into the holistic transportation system for a safe
efficient
comfortable and environment friendly transport ecological construction. A variety of ITS applications and services have emerged in the intelligent traffic management system
autonomous driving system
and cooperative vehicle-infrastructure system (CVIS). To realise the intelligentisation of the traffic management
the intelligent traffic management system is mainly supported by optimised smart traffic facilities. Autonomous driving is mainly based on vehicle intelligence
relying on the cooperation of visual perception
radar perception
positioning system
on-board computing and artificial intelligence. The CVIS depends on the collaborative work of intelligent vehicles
roadside equipment and cloud platforms in support of the internet of vehicles (IOV) to fully implement dynamic real-time information interaction between vehicles and related traffic issues to achieve the vehicle-road collaborative management and traffic safety. Image processing is one of the core technologies that can support the deployment of a large number of ITS applications. It is based on computer algorithms application to extract useful visual sensor data derived information
including image enhancement and restoration
feature extraction and classification
as well as the semantic and instance segmentation. Image enhancement and restoration refers to improve the visual performance of the image and render more suitable image for human or machine analysis to deal with the image quality reduction issues of the challenging traffic system scenarios. Image feature extraction and classification technology is the core of object detection for accurate images or videos derived traffic objects locating and identifying
which is the basis for solving more complex higher-level tasks such as segmentation
scene understanding
object tracking
image description
event detection and activity recognition. To achieve higher-precision environmental perception in ITS
the semantic and instance segmentation realises pixel-level image classification based on scene semantic information in terms of the provision of different labels for instances of the same category. These image processing technologies can illustrate crucial references and information to enhance the capabilities of the perception
recognition
object detection
tracking
and path planning modules for more ITS applications. The multifaceted scenarios integration provides important technical support for intelligent traffic management
autonomous driving and the CVIS. In addition
the wide deployment of sensing devices places tremendous demands on data transmission and processing. As the widely recognised data processing technology
the centralised cloud computing is challenged to meet the real-time requirements of most massive data applications in ITS
which leads to uncertainty and barriers in the transmission process. Differentiated from the centralised cloud computing
the multi-access edge computing (MEC) technology deploys sensing
computation
and storage resources close to the network edge and provide a low-latency response-based platform for ITS
high bandwidth
and real-time applications and services access to network information. Our research reviews the current development status and typical applications of ITS. We focus on current image processing and MEC technologies for ITS. Future research direction of ITS and its related image processing and MEC technologies are predicted.
智慧交通系统(ITS)图像处理边缘计算自动驾驶车路协同(CVIS)深度学习
intelligent transportation system(ITS)image processingedge computingautonomous drivingcooperative vehicle-infrastructure system(CVIS)deep learning
Ahmed A and Ahmed E. 2016. A survey on mobile edge computing//Proceedings of the 10th International Conference on Intelligent Systems and Control (ISCO). Coimbatore, India: IEEE: 1-8 [DOI: 10.1109/ISCO.2016.7727082http://dx.doi.org/10.1109/ISCO.2016.7727082]
Ali M, Anjum A, Rana O, Zamani A R, Balouek-Thomert D and Parashar M. 2020. RES: real-time video stream analytics using edge enhanced clouds. IEEE Transactions on Cloud Computing (Early Access [DOI: 10.1109/TCC.2020.2991748http://dx.doi.org/10.1109/TCC.2020.2991748]
Badrinarayanan V, Kendall A and Cipolla R. 2017. SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12): 2481-2495 [DOI: 10.1109/TPAMI.2016.2644615]
Benjumea A, Teeti I, Cuzzolin F and Bradley A. 2021. YOLO-Z: improving small object detection in YOLOv5 for autonomous vehicles[EB/OL]. [2021-12-31].https://arxiv.org/pdf/2112.11798.pdfhttps://arxiv.org/pdf/2112.11798.pdf
Bochkovskiy A, Wang C Y and Liao H Y M. 2020. YOLOv4: optimal speed and accuracy of object detection [EB/OL]. [2021-12-31].https://arxiv.org/pdf/2004.10934.pdfhttps://arxiv.org/pdf/2004.10934.pdf
Boykov Y Y and Jolly M P. 2001. Interactive graph cuts for optimal boundary and region segmentation of objects in N-D images//Proceedings of the 8th IEEE International Conference on Computer Vision. Vancouver, Canada: IEEE: 105-112 [DOI: 10.1109/ICCV.2001.937505http://dx.doi.org/10.1109/ICCV.2001.937505]
Buchsbaum G. 1980. A spatial processor model for object colour perception. Journal of the Franklin Institute, 310(1): 1-26 [DOI: 10.1016/0016-0032(80)90058-7]
Cai J R, Gu S H and Zhang L. 2018. Learning a deep single image contrast enhancer from multi-exposure images. IEEE Transactions on Image Processing, 27(4): 2049-2062 [DOI: 10.1109/TIP.2018.2794218]
Carreira J, Caseiro R, Batista J and Sminchisescu C. 2012. Semantic segmentation with second-order pooling//Proceedings of the 12th European Conference on Computer Vision. Firencze, Italy: Springer: 430-443 [DOI: 10.1007/978-3-642-33786-4_32http://dx.doi.org/10.1007/978-3-642-33786-4_32]
Chen C, Chen Q F, Xu J and Koltun V. 2018. Learning to see in the dark//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 3291-3300 [DOI: 10.1109/CVPR.2018.00347http://dx.doi.org/10.1109/CVPR.2018.00347]
Chen G, Wang F, Qu S Q, Chen K, Yu J W, Liu X Y, Xiong L and Knoll A. 2021a. Pseudo-image and sparse points: vehicle detection with 2D LiDAR revisited by deep learning-based methods. IEEE Transactions on Intelligent Transportation Systems, 22(12): 7699-7711 [DOI: 10.1109/TITS.2020.3007631]
Chen Q, Wang Y M, Yang T, Zhang X Y, Cheng J and Sun J. 2021b. You only look one-level feature//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville, USA: IEEE: 13034-13043 [DOI: 10.1109/CVPR46437.2021.01284http://dx.doi.org/10.1109/CVPR46437.2021.01284]
Chen C, Liu B, Wan S H, Qiao P and Pei Q Q. 2021c. An edge traffic flow detection scheme based on deep learning in an intelligent transportation system. IEEE Transactions on Intelligent Transportation Systems, 22(3): 1840-1852 [DOI: 10.1109/TITS.2020.3025687]
Chen L C, Papandreou G, Kokkinos I, Murphy K and Yuille A L. 2014. Semantic image segmentation with deep convolutional nets and fully connected CRFs [EB/OL]. [2021-12-31].https://arxiv.org/pdf/1412.7062.pdfhttps://arxiv.org/pdf/1412.7062.pdf
Cui J, Wei L, Zhong H, Zhang J, Xu Y and Liu L. 2020. Edge computing in VANETs——an efficient and privacy-preserving cooperative downloading scheme. IEEE Journal on Selected Areas in Communications, 38(6): 1191-1204 [DOI: 10.1109/JSAC.2020.2986617]
Dai J F, Qi H Z, Xiong Y W, Li Y, Zhang G D, Hu H and Wei Y C. 2017. Deformable convolutional networks//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, Italy: IEEE: 764-773 [DOI: 10.1109/ICCV.2017.89http://dx.doi.org/10.1109/ICCV.2017.89]
Dalal N and Triggs B. 2005. Histograms of oriented gradients for human detection//Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, USA: IEEE: 886-893 [DOI: 10.1109/CVPR.2005.177http://dx.doi.org/10.1109/CVPR.2005.177]
Deng L Y, Yang M, Qian Y Q, Wang C X and Wang B. 2017. CNN based semantic segmentation for urban traffic scenes using fisheye camera//2017 IEEE Intelligent Vehicles Symposium (IV). Los Angeles, USA: IEEE: 231-236 [DOI: 10.1109/IVS.2017.7995725http://dx.doi.org/10.1109/IVS.2017.7995725]
Deng S G, Zhao H L, Fang W J, Yin J W, Dustdar S and Zomaya A Y. 2020. Edge intelligence: the confluence of edge computing and artificial intelligence. IEEE Internet of Things Journal, 7(8): 7457-7469 [DOI: 10.1109/JIOT.2020.2984887]
Dewi C, Chen R C, Liu Y T, Jiang X Y and Hartomo K D. 2021. Yolo V4 for advanced traffic sign recognition with synthetic training data generated by various GAN. IEEE Access, 9: 97228-97242 [DOI: 10.1109/ACCESS.2021.3094201]
Du S D, Li T R, Gong X and Horng S J. 2018. A hybrid method for traffic flow forecasting using multimodal deep learning [EB/OL]. [2021-12-31].https://arxiv.org/pdf/1803.02099.pdfhttps://arxiv.org/pdf/1803.02099.pdf
Emara M, Filippou M C and Sabella D. 2018. MEC-assisted end-to-end latency evaluations for C-V2X communications//Proceedings of 2018 European Conference on Networks and Communications (EuCNC). Ljubljana, Slovenia: IEEE: 1-9 [DOI: 10.1109/EuCNC.2018.8442825http://dx.doi.org/10.1109/EuCNC.2018.8442825]
Engin D, GençA and Ekenel H K. 2018. Cycle-Dehaze: enhanced CycleGAN for single image dehazing//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Salt Lake City, USA: IEEE: 825-833 [DOI: 10.1109/CVPRW.2018.00127http://dx.doi.org/10.1109/CVPRW.2018.00127]
Fan H Y, Zhu F, Liu C C, Zhang L L, Zhuang L, Li D, Zhu W C, Hu J T, Li H Y and Kong Q. 2018. Baidu Apollo EM motion planner[EB/OL]. [2021-12-31].https://arxiv.org/pdf/1807.08048.pdfhttps://arxiv.org/pdf/1807.08048.pdf
Felzenszwalb P, McAllester D and Ramanan D. 2008. A discriminatively trained, multiscale, deformable part model//Proceedings of 2008 IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, USA: IEEE: 1-8 [DOI: 10.1109/CVPR.2008.4587597http://dx.doi.org/10.1109/CVPR.2008.4587597]
Gao W S, Zhang X G, Yang L and Liu H Z. 2010. An improved Sobel edge detection//Proceedings of the 3rd International Conference on Computer Science and Information Technology. Chengdu, China: IEEE: 67-71 [DOI: 10.1109/ICCSIT.2010.5563693http://dx.doi.org/10.1109/ICCSIT.2010.5563693]
Ge Z, Liu S T, Wang F, Li Z M and Sun J. 2021. YOLOX: exceeding YOLO series in 2021[EB/OL]. [2021-12-31].https://arxiv.org/pdf/2107.08430.pdfhttps://arxiv.org/pdf/2107.08430.pdf
Ghiasi G and Fowlkes C C. 2016. Laplacian pyramid reconstruction and refinement for semantic segmentation//Proceedings of the 14th European Conference on Computer Vision. Amsterdam, the Netherlands: Springer: 519-534 [DOI: 10.1007/978-3-319-46487-9_32http://dx.doi.org/10.1007/978-3-319-46487-9_32]
Girshick R. 2015. Fast R-CNN//Proceedings of 2015 IEEE International Conference on Computer Vision (ICCV). Santiago, Chile: IEEE: 1440-1448 [DOI: 10.1109/ICCV.2015.169http://dx.doi.org/10.1109/ICCV.2015.169]
Girshick R, Donahue J, Darrell T and Malik J. 2014. Rich feature hierarchies for accurate object detection and semantic segmentation//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, USA: IEEE: 580-587 [DOI: 10.1109/cvpr.2014.81http://dx.doi.org/10.1109/cvpr.2014.81]
Giust F, Costa-Perez X and Reznik A. 2017. Multi-access edge computing: an overview of ETSI MEC ISG. IEEE 5G Tech Focus, 1(4): #4
Gu M, Zheng L T and You Z. 2015. Traffic image enhancement algorithm based on color space conversion. Chinese Journal of Scientific Instrument, 36(8): 1901-1907
顾明, 郑林涛, 尤政. 2015. 基于颜色空间转换的交通图像增强算法. 仪器仪表学报, 36(8): 1901-1907 [DOI: 10.19650/j.cnki.cjsi.2015.08.028]
Gudigar A, Chokkadi S and U R. 2016. A review on automatic detection and recognition of traffic sign. Multimedia Tools and Applications, 75(1): 333-364 [DOI: 10.1007/s11042-014-2293-7]
Guo K Y, Li X, Zhang M, Bao Q Cand Yang M. 2021. Real-time vehicle object detection method based on multi-scale feature fusion. IEEE Access, 9: 115126-115134 [DOI: 10.1109/ACCESS.2021.3104849]
He K M, Zhang X Y, Ren S Q and Sun J. 2015. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(9): 1904-1916 [DOI: 10.1109/TPAMI.2015.2389824]
Hooper J. 2004. From DARPA grand challenge 2004: DARPA's debacle in the desert [EB/OL]. [2021-12-31].https://www.popsci.comhttps://www.popsci.com
Hu J W, Zheng B Y, Wang C, Zhao C H, Hou X L, Pan Q and Xu Z. 2020. A survey on multi-sensor fusion based obstacle detection for intelligent ground vehicles in off-road environments. Frontiers of Information Technology and Electronic Engineering, 21(5): 675-692 [DOI: 10.1631/FITEE.1900518]
Huang X G, Xu K, Lai C B, Chen Q B and Zhang J. 2020. Energy-efficient offloading decision-making for mobile edge computing in vehicular networks. EURASIP Journal on Wireless Communications and Networking, 2020(1): #35 [DOI: 10.1186/s13638-020-1652-5]
Huang X M, Li P C and Yu R. 2019. Social welfare maximization in container-based task scheduling for parked vehicle edge computing. IEEE Communications Letters, 23(8): 1347-1351 [DOI: 10.1109/LCOMM.2019.2920832]
Hummel R. 1977. Image enhancement by histogram transformation. Computer Graphics and Image Processing, 6(2): 184-195 [DOI: 10.1016/S0146-664X(77)80011-7]
Huo Z Q, Xia Y Z and Zhang B L. 2016. Vehicle type classification and attribute prediction using multi-task RCNN//Proceedings of the 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). Datong, China: IEEE: 564-569 [DOI: 10.1109/CISP-BMEI.2016.7852774http://dx.doi.org/10.1109/CISP-BMEI.2016.7852774]
Ingle S and Phute M. 2016. Tesla autopilot: semi autonomous driving, an uptick for future autonomy. International Research Journal of Engineering and Technology, 3(9): 369-372
Jiang Y F, Gong X Y, Liu D, Cheng Y, Fang C, Shen X H, Yang J C, Zhou P and Wang Z Y. 2021. EnlightenGAN: deep light enhancement without paired supervision. IEEE Transactions on Image Processing, 30: 2340-2349 [DOI: 10.1109/TIP.2021.3051462]
Kanimozhi S, Gayathri G and Mala T. 2019. Multiple real-time object identification using single shot multi-box detection//Proceedings of 2019 International Conference on Computational Intelligence in Data Science (ICCIDS). Chennai, India: IEEE: 1-5 [DOI: 10.1109/ICCIDS.2019.8862041http://dx.doi.org/10.1109/ICCIDS.2019.8862041]
Kanwal M, Malik A W, Rahman A U, Mahmood I and Shahzad M. 2020. Sustainable vehicle-assisted edge computing for big data migration in smart cities. IEEE Internet of Things Journal, 7(3): 1857-1871 [DOI: 10.1109/JIOT.2019.2957127]
Kiyasu K and Okutani T. 2005. Endeavor for ITS in Japan. Ann. Télécommun, 60: 264-280 [DOI: 10.1007/BF03219821]
Kuang H L, Zhang X S, Li Y J, Chan L L H and Yan H. 2017. Nighttime vehicle detection based on bio-inspired image enhancement and weighted score-level feature fusion. IEEE Transactions on Intelligent Transportation Systems, 18(4): 927-936 [DOI: 10.1109/TITS.2016.2598192]
Land E H. 1986. An alternative technique for the computation of the designator in the retinex theory of color vision. Proceedings of the National Academy of Sciences of the United States of America, 83(10): 3078-3080 [DOI: 10.1073/pnas.83.10.3078]
Li E S, Zhu S L, Zhu B S, Zhao Y, Xia C G and Song L H. 2009. An adaptive edge-detection method based on the canny operator//Proceedings of 2009 International Conference on Environmental Science and Information Application Technology. Wuhan, China: IEEE: 465-469 [DOI: 10.1109/ESIAT.2009.49http://dx.doi.org/10.1109/ESIAT.2009.49]
Li G F, Yang Y F, Qu X D, Cao D P and Li K Q. 2021a. A deep learning based image enhancement approach for autonomous driving at night. Knowledge-Based Systems, 213: #106617 [DOI:10.1016/j.knosys.2020.106617]
Li L J, Zhou H M, Xiong S X, Yang J J and Mao Y M. 2019. Compound model of task arrivals and load-aware offloading for vehicular mobile edge computing networks. IEEE Access, 7: 26631-26640 [DOI: 10.1109/ACCESS.2019.2901280]
Li M D, Liu J Y, Yang W H, Sun X Y and Guo Z M. 2018. Structure-revealing low-light image enhancement via robust retinex model. IEEE Transactions on Image Processing, 27(6): 2828-2841 [DOI: 10.1109/TIP.2018.2810539]
Li Y J, Yang S, Zheng Y C and Lu H M. 2021b. Improved point-voxel region Convolutional Neural Network: 3D Object Detectors for Autonomous Driving. IEEE Transactions on Intelligent Transportation Systems, #3071790 [DOI: 10.1109/TITS.2021.3071790http://dx.doi.org/10.1109/TITS.2021.3071790]
Lin Y X, Wang P and Ma M. 2017a. Intelligent Transportation System (ITS): concept, challenge and opportunity//Proceeding of 2017 IEEE 3rd International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing (HPSC), and IEEE International Conference on Intelligent Data and Security (IDS). Beijing, China: IEEE: 167-172 [DOI: 10.1109/BigDataSecurity.2017.50http://dx.doi.org/10.1109/BigDataSecurity.2017.50]
Lin T Y, Dollár P, Girshick R, He K, Hariharan B and Belongie S. 2017b. Feature pyramid networks for object detection//Proceedings of 2017 IEEE conference on computer vision and pattern recognition (CVPR). Honolulu, USA: 936-944 [DOI: 10.1109/CVPR.2017.106http://dx.doi.org/10.1109/CVPR.2017.106]
Lin T Y, Goyal P, Girshick R, He K M and Dollár P. 2017c. Focal loss for dense object detection//Proceedings of 2017 IEEE International Conference on Computer Vision (ICCV). Venice, Italy: IEEE: 2980-2988 [DOI: 10.1109/ICCV.2017.324http://dx.doi.org/10.1109/ICCV.2017.324]
Lin G S, Milan A, Shen C H and Reid L. 2017d. RefineNet: multi-path refinement networks for high-resolution semantic segmentation//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE: 5168-5177 [DOI: 10.1109/CVPR.2017.549http://dx.doi.org/10.1109/CVPR.2017.549]
Liu T T, Xu H, Shi J P and Wen F Q. 2020. Fast logistics vehicle localizing based on EMVS-MIMO radar and edge computing. IEEE Access, 8: 200705-200713 [DOI: 10.1109/ACCESS.2020.3036064]
Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C Y and Berg A C. 2016. SSD: single shot MultiBox detector//Proceedings of the 14th European Conference on Computer Vision. Amsterdam, the Netherlands: Springer: 21-37 [DOI: 10.1007/978-3-319-46448-0_2http://dx.doi.org/10.1007/978-3-319-46448-0_2]
Liu Y, Yu H M, Xie S L and Zhang Y. 2019. Deep reinforcement learning for offloading and resource allocation in vehicle edge computing and networks. IEEE Transactions on Vehicular Technology, 68(11): 11158-11168 [DOI: 10.1109/TVT.2019.2935450]
Long J, Shelhamer E and Darrell T. 2015. Fully convolutional networks for semantic segmentation//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA: IEEE: 3431-3440 [DOI: 10.1109/CVPR.2015.7298965http://dx.doi.org/10.1109/CVPR.2015.7298965]
Ma Y C, Chowdhury M, Sadek A and Jeihani M. 2009. Real-time highway traffic condition assessment framework using Vehicle——Infrastructure Integration (VII) with Artificial Intelligence (AI). IEEE Transactions on Intelligent Transportation Systems, 10(4): 615-627 [DOI: 10.1109/TITS.2009.2026673]
Malinverno M, Mangues-Bafalluy J, Casetti C E, Chiasserini C F, Requena-Esteso M and Baranda J. 2020. An edge-based framework for enhanced road safety of connected cars. IEEE Access, 8: 58018-58031 [DOI: 10.1109/ACCESS.2020.2980902]
Misener J A and Shladover S E. 2006. PATH investigations in vehicle-roadside cooperation and safety: a foundation for safety and vehicle-infrastructure integration research//Proceedings of 2006 IEEE Intelligent Transportation Systems Conference. Toronto, Canada: IEEE: 9-16 [DOI: 10.1109/ITSC.2006.1706711http://dx.doi.org/10.1109/ITSC.2006.1706711]
Møgelmose A, Liu D R and Trivedi M M. 2015. Detection of U.S. traffic signs. IEEE Transactions on Intelligent Transportation Systems, 16(6): 3116-3125 [DOI: 10.1109/TITS.2015.2433019]
Murugan V, Vijaykumar V R and Nidhila A. 2019. Vehicle logo recognition using RCNN for intelligent transportation systems//Proceedings of 2019 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET). Chennai, India: IEEE: 107-111 [DOI: 10.1109/WiSPNET45539.2019.9032733http://dx.doi.org/10.1109/WiSPNET45539.2019.9032733]
Nguyen R M H, Kim S J and Brown M S. 2014. Illuminant aware gamut-based color transfer. Computer Graphics Forum, 33(7): 319-328 [DOI: 10.1111/cgf.12500]
Nikodem M, Słabicki M, Surmacz T, Mrówka P and Dołęga C. 2020. Multi-camera vehicle tracking using edge computing and low-power communication. Sensors, 20(11): #3334 [DOI: 10.3390/s20113334]
Noh H, Hong S and Han B. 2015. Learning deconvolution network for semantic segmentation//Proceedings of 2015 IEEE International Conference on Computer Vision. Santiago, Chile: IEEE: 1520-1528 [DOI: 10.1109/ICCV.2015.178http://dx.doi.org/10.1109/ICCV.2015.178]
Olaverri-Monreal C, García-Fernández F and Rossetti R J F. 2020. A-RCRAFT framework for analysing automation: application to SAE J3016 levels of driving automation//Olaverri-Monreal C, García-Fernández F and Rossetti R J F, eds. Human Factors in Intelligent Vehicles. Gistrup, Denmark: River Publishers: 59-82
Ooi C H and Isa N A M. 2010. Adaptive contrast enhancement methods with brightness preserving. IEEE Transactions on Consumer Electronics, 56(4): 2543-2551 [DOI: 10.1109/TCE.2010.5681139]
Pan J W, Cui J, Wei L, Xu Y and Zhong H. 2019. Secure data sharing scheme for VANETs based on edge computing. EURASIP Journal on Wireless Communications and Networking, 2019(1): #169 [DOI: 10.1186/s13638-019-1494-1]
Pang S C, Wang N L, Wang M, Qiao S B, Zhai X and Xiong N N. 2021. A smart network resource management system for high mobility edge computing in 5G internet of vehicles. IEEE Transactions on Network Science and Engineering, 8(4): 3179-3191 [DOI: 10.1109/TNSE.2021.3106955]
Park J G and Kim K J. 2013. Design of a visual perception model with edge-adaptive Gabor filter and support vector machine for traffic sign detection. Expert Systems with Applications, 40(9): 3679-3687 [DOI: 10.1016/j.eswa.2012.12.072]
Paszke A, Chaurasia A, Kim S and Culurciello E. 2016. ENet: a deep neural network architecture for real-time semantic segmentation [EB/OL]. [2021-12-31].https://arxiv.org/pdf/1606.02147.pdfhttps://arxiv.org/pdf/1606.02147.pdf
Pont-Tuset J, Arbeláez P, Barron J T, Marques F and Malik J. 2017. Multiscale combinatorial grouping for image segmentation and object proposal generation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(1): 128-140 [DOI: 10.1109/TPAMI.2016.2537320]
Qi Q, Wang J Y, Ma Z Y, Sun H F, Cao Y F, Zhang L X, and Liao J X. 2019. Knowledge-driven service offloading decision for vehicular edge computing: a deep reinforcement learning approach. IEEE Transactions on Vehicular Technology, 68(5): 4192-4203 [DOI: 10.1109/TVT.2019.2894437]
Qiao G H, Leng S P, Maharjan S, Zhang Y and Ansari N. 2020. Deep reinforcement learning for cooperative content caching in vehicular edge computing and networks. IEEE Internet of Things Journal, 7(1): 247-257 [DOI: 10.1109/JIOT.2019.2945640]
Ravì D, Bober M, Farinella G M, Guarnera M and Battiato S. 2016. Semantic segmentation of images exploiting DCT based features and random forest. PatternRecognition, 52: 260-273 [DOI: 10.1016/j.patcog.2015.10.021]
Raza S, Liu W, Ahmed M, Anwar M R, Mirza M A, Sun Q B and Wang S G. 2020. An efficient task offloading scheme in vehicular edge computing. Journal of Cloud Computing, 9(1): #28 [DOI: 10.1186/s13677-020-00175-w]
Redmon J, Divvala S, Girshick R and Farhadi A. 2016. You only look once: unified, real-time object detection//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, USA: IEEE: 779-788 [DOI: 10.1109/CVPR.2016.91http://dx.doi.org/10.1109/CVPR.2016.91]
Ren S Q, He K M, Girshick R and Sun J. 2017. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6): 1137-1149 [DOI: 10.1109/TPAMI.2016.2577031]
Roberts J J. 2020. "Road of the future" to link Detroit and Ann Arbor with 40 miles of driverless cars and shuttles [EB/OL]. [2021-12-31].https://fortune.com/2020/08/13/road-of-the-future-detroit-ann-arbor-driverless-cars-autonomous-vehicles/https://fortune.com/2020/08/13/road-of-the-future-detroit-ann-arbor-driverless-cars-autonomous-vehicles/
Ronneberger O, Fischer P and Brox T. 2015. U-Net: convolutional networks for biomedical image segmentation//Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich, Germany: Springer: 234-241 [DOI: 10.1007/978-3-319-24574-4_28http://dx.doi.org/10.1007/978-3-319-24574-4_28]
Rosenband D L. 2017. Inside Waymo's self-driving car: my favorite transistors//Proceedings of 2017 Symposium on VLSI Circuits. Kyoto, Japan: IEEE: C20-C22 [DOI: 10.23919/VLSIC.2017.8008500http://dx.doi.org/10.23919/VLSIC.2017.8008500]
Rother C, Kolmogorov V and Blake A. 2004. "GrabCut": interactive foreground extraction using iterated graph cuts. ACM Transactions on Graphics, 23(3): 309-314 [DOI: 10.1145/1015706.1015720]
Scharwächter T and Franke U. 2015. Low-level fusion of color, texture and depth for robust road scene understanding//2015 IEEE Intelligent Vehicles Symposium. Seoul, Korea(South): IEEE: 599-604 [DOI: 10.1109/IVS.2015.7225750http://dx.doi.org/10.1109/IVS.2015.7225750]
Shao C Y, Zhang L M and Pan W. 2021. Faster R-CNN learning-based semantic filter for geometry estimation and its applicationin vSLAM systems. IEEE Transactions on Intelligent Transportation Systems [J/OL]. [2021-12-31].https://ieeexplore.ieee.org/document/9339874 [DOI: 10.1109/TITS.2021.3052812]https://ieeexplore.ieee.org/document/9339874[DOI:10.1109/TITS.2021.3052812]
Shuai Q J and Wu X W. 2020. Object detection system based on SSD algorithm//Proceedings of 2020 International Conference on Culture-oriented Science and Technology (ICCST). Beijing, China: IEEE: 141-144 [DOI: 10.1109/ICCST50977.2020.00033http://dx.doi.org/10.1109/ICCST50977.2020.00033]
Steyer S, Lenk C, Kellner D, Tanzmeister G and Wollherr D. 2020. Grid-based object tracking with nonlinear dynamic state and shape estimation. IEEE Transactions on Intelligent Transportation Systems, 21(7): 2874-2893 [DOI: 10.1109/TITS.2019.2921248]
Su C L, Lai W C and Li C T. 2021. Pedestrian detection system with edge computing integration on embedded vehicle//Proceedings of 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). Jeju Island, Korea(South): IEEE: 450-453 [DOI: 10.1109/ICAIIC51459.2021.9415262http://dx.doi.org/10.1109/ICAIIC51459.2021.9415262]
Su C L, Lai W C, Zhang Y K, Guo T J, Hung Y J and Chen H C. 2020. Artificial intelligence design on embedded board with edge computing for vehicle applications//Proceedings of 3rd IEEE International Conference on Artificial Intelligence and Knowledge Engineering (AIKE). Laguna Hills, USA: IEEE: 130-133 [DOI: 10.1109/AIKE48582.2020.00026http://dx.doi.org/10.1109/AIKE48582.2020.00026]
Sugimoto Y and Kuzumaki S. 2019. SIP-adus: An update on Japanese initiatives for automated driving. In: Road Vehicle Automation 5. Lecture Notes in Mobility. Springer, Cham: 17-26 [DOI: 10.1007/978-3-319-94896-6_2http://dx.doi.org/10.1007/978-3-319-94896-6_2]
Sun J N, Gu Q, Zheng T, Dong P and Qin Y J. 2019. Joint communication and computing resource allocation in vehicular edge computing. International Journal of Distributed Sensor Networks, 15(3): 1-13 [DOI: 10.1177/1550147719837859]
Tariq A, Khan M Z and Khan M U G. 2021. Real time vehicle detection and colour recognition using tuned features of Faster-RCNN//Proceedings of the 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA). Riyadh, Saudi Arabia: IEEE: 262-267 [DOI: 10.1109/CAIDA51941.2021.9425106http://dx.doi.org/10.1109/CAIDA51941.2021.9425106]
Tian D X, Lin C M, Zhou J S, Duan X T, Cao Y, Zhao D Z and Cao D P. 2020. SA-YOLOv3: an efficient and accurate object detector using self-attention mechanism for autonomous driving. IEEE Transactions on Intelligent Transportation Systems (Early Access): #3041278 [DOI: 10.1109/TITS.2020.3041278http://dx.doi.org/10.1109/TITS.2020.3041278]
Tian F, Wu C Z, Chu D F, Sun C and Zhou T Q. 2014. Experimental design of integrated platform for demonstration of cooperative vehicle infrastructure systems in China//Proceedings of the 17th International IEEE Conference on Intelligent Transportation Systems (ITSC). Qingdao, China: IEEE: 105-108 [DOI: 10.1109/ITSC.2014.6957674http://dx.doi.org/10.1109/ITSC.2014.6957674]
Vázquez-Gallego F, Vilalta R, García A, Mira F, Vía S, Muñoz R, Alonso-Zarate J and Catalan-Cid M. 2019. Demo: a mobile edge computing-based collision avoidance system for future vehicular networks//Proceedings of 2019 IEEE INFOCOM 2019-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). Paris, France: IEEE: 904-905 [DOI: 10.1109/INFCOMW.2019.8845107http://dx.doi.org/10.1109/INFCOMW.2019.8845107]
Vezhnevets A, Buhmann J M and Ferrari V. 2012. Active learning for semantic segmentation with expected change//Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA: IEEE: 3162-3169 [DOI: 10.1109/CVPR.2012.6248050http://dx.doi.org/10.1109/CVPR.2012.6248050]
Viola P and Jones M. 2001. Rapid object detection using a boosted cascade of simple features//Proceedings of 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Kauai, USA: IEEE: 511-518 [DOI: 10.1109/CVPR.2001.990517http://dx.doi.org/10.1109/CVPR.2001.990517]
Wan S H, Li X, Xue Y, Lin W M and Xu X L. 2020. Efficient computation offloading for Internet of Vehicles in edge computing-assisted 5G networks. The Journal of Supercomputing, 76(4): 2518-2547 [DOI: 10.1007/s11227-019-03011-4]
Wang J B, Lu K, Xue J, He N and Shao L. 2018. Single image dehazing based on the physical model and MSRCR algorithm. IEEE Transactions on Circuits and Systems for Video Technology, 28(9): 2190-2199 [DOI: 10.1109/TCSVT.2017.2728822]
Wang Z J, Wu Y and Niu Q Q. 2019. Multi-sensor fusion in automated driving: a survey. IEEE Access, 8: 2847-2868 [DOI: 10.1109/ACCESS.2019.2962554]
Wen Y, Lu Y, Yan J Q, Zhou Z Y, von Deneen K M and Shi P F. 2011. An algorithm for license plate recognition applied to intelligent transportation system. IEEE Transactions on Intelligent Transportation Systems, 12(3): 830-845 [DOI: 10.1109/TITS.2011.2114346]
Wu Z Z. 2010. Welcome the new era of intelligent transportation in China. Science, 62(1): 3-6
吴忠泽. 2010. 迎接中国智能交通的新时代. 科学, 62(1): 3-6 [DOI: 10.3969/j.issn.0368-6396.2010.01.002]
Xu X L, Xue Y, Li X, Qi L Y and Wan S H. 2019. A computation offloading method for edge computing with vehicle-to-everything. IEEE Access, 7: 131068-131077 [DOI: 10.1109/ACCESS.2019.2940295]
Yang S R, Su Y J, Chang Y Y and Hung H N. 2019. Short-term traffic prediction for edge computing-enhanced autonomous and connected cars. IEEE Transactions on Vehicular Technology, 68(4): 3140-3153 [DOI: 10.1109/TVT.2019.2899125]
Yao Z H, Shen L O, Liu R H, Jiang Y S and Yang X G. 2020. A dynamic predictive traffic signal control framework in a cross-sectional vehicle infrastructure integration environment. IEEE Transactions on Intelligent Transportation Systems, 21(4): 1455-1466 [DOI: 10.1109/TITS.2019.2909390]
Yen J C, Chang F J and Chang S. 1995. A new criterion for automatic multilevel thresholding. IEEE Transactions on Image Processing, 4(3): 370-378 [DOI: 10.1109/83.366472]
Young M S, Birrell S A and Stanton N A. 2011. Safe driving in a green world: a review of driver performance benchmarks and technologies to support "smart" driving. Applied Ergonomics, 42(4): 533-539 [DOI: 10.1016/j.apergo.2010.08.012]
Yu F and Koltun V. 2016. Multi-scale context aggregation by dilated convolutions [EB/OL]. [2021-12-31].https://arxiv.org/pdf/1511.07122.pdfhttps://arxiv.org/pdf/1511.07122.pdf
Yu S Y and Zhu H. 2019. Low-illumination image enhancement algorithm based on a physical lighting model. IEEE Transactions on Circuits and Systems for Video Technology, 29(1): 28-37 [DOI: 10.1109/TCSVT.2017.2763180]
Zhang D G, Gong C L, Zhang T, Zhang J and Piao M J. 2021. A new algorithm of clustering AODV based on edge computing strategy in IOV. Wireless Networks, 27(4): 2891-2908 [DOI: 10.1007/s11276-021-02624-z]
Zhang J P, Wang F Y, Wang K F, Lin W H, Xu X and Chen C. 2011. Data-driven intelligent transportation systems: a survey. IEEE Transactions on Intelligent Transportation Systems, 12(4): 1624-1639 [DOI: 10.1109/TITS.2011. 2158001]
Zhang J X, Huang X M and Yu R. 2020. Optimal task assignment with delay constraint for parked vehicle assisted edge computing: a stackelberg game approach. IEEE Communications Letters, 24(3): 598-602 [DOI: 10.1109/LCOMM.2019.2961663]
Zhang Y and Larson K. 2018. CityScope—application of tangible interface, augmented reality, and artificial intelligence in the Urban decision support system. Time + Architecture, (1): 44-49
张砚, 肯特·蓝森. 2018. CityScope—可触交互界面、增强现实以及人工智能于城市决策平台之运用. 时代建筑, (1): 44-49 [DOI: 10.13717/j.cnki.ta.2018.01.009]
Zhao H S, Shi J P, Qi X J, Wang X G and Jia J Y. 2017. Pyramid scene parsing network//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE: 6230-6239 [DOI: 10.1109/CVPR.2017.660http://dx.doi.org/10.1109/CVPR.2017.660]
Zhao Y, Tian Y H, Dang J W, Fu S J, Wang H Y, Wan J, An G Y, Du Z R, Liao L X and Wei S K. 2021. Frontiers of transportation video structural analysis in the smart city. Journal of Image and Graphics, 26(6): 1227-1253
赵耀, 田永鸿, 党建武, 付树军, 王恒友, 万军, 安高云, 杜卓然, 廖理心, 韦世奎. 2021. 面向智慧城市的交通视频结构化分析前沿进展. 中国图象图形学报, 26(6): 1227-1253 [DOI: 10.11834/jig.210035]
Zhou Z, Chen X, Li E, Zeng L K, Luo K and Zhang J S. 2019a. Edge intelligence: paving the last mile of artificial intelligence with edge computing. Proceedings of the IEEE, 107(8): 1738-1762 [DOI: 10.1109/JPROC.2019.2918951]
Zhou Z Y, Feng J H, Chang Z and Shen X M. 2019b. Energy-efficient edge computing service provisioning for vehicular networks: a consensus ADMM approach. IEEE Transactions on Vehicular Technology, 68(5): 5087-5099 [DOI: 10.1109/TVT.2019.2905432]
Zhu F, Ma L, Xu X, Guo D F, Cui X and Kong Q. 2018. Baidu Apollo auto-calibration system - an industry-level data-driven and learning based vehicle longitude dynamic calibrating algorithm[EB/OL]. [2021-12-31].https://arxiv.org/pdf/1808.10134.pdfhttps://arxiv.org/pdf/1808.10134.pdf
Zhu Q, Yeh M C, Cheng K T and Avidan S. 2006. Fast human detection using a cascade of histograms of oriented gradients//Proceedings of 2016 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE: 1491-1498 [DOI: 10.1109/CVPR.2006.119http://dx.doi.org/10.1109/CVPR.2006.119]
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