Lane line detection and departure estimation in a complex environment by using an asymmetric kernel convolution algorithm
Deep learning has made tremendous advances in the domains of image segmentation and object classification. However, real-time lane line detection and departure estimates in complex traffic conditions have proven to be hard in autonomous driving research. Traditional lane line detection methods requi...
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| Veröffentlicht in: | The Visual computer Jg. 39; H. 2; S. 519 - 538 |
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01.02.2023
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| Abstract | Deep learning has made tremendous advances in the domains of image segmentation and object classification. However, real-time lane line detection and departure estimates in complex traffic conditions have proven to be hard in autonomous driving research. Traditional lane line detection methods require manual parameter modification, but they have some limitations that are still susceptible to interference from obscuring objects, lighting changes, and pavement deterioration. The development of accurate lane line detection and departure estimate algorithms is still a challenge. This article investigated a convolutional neural network (CNN) for lane line detection and departure estimate in a complicated road environment. CNN includes a weight-sharing function that lowers the training parameters. CNN can learn and extract features frequently in image segmentation, object detection, classification, and other applications. The symmetric kernel convolution of classical CNN is upgraded to the structure of asymmetric kernel convolution (AK-CNN) based on lane line detection and departure estimation features. It reduces the CNN network's computational load and improves the speed of lane line detection and departure estimates. The experiment was carried out on the CULane dataset. The lane line detection results have high accuracy in a complex environment by 80.3%. The detection speed is 84.5 fps, which enables real-time lane line detection. |
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| AbstractList | Deep learning has made tremendous advances in the domains of image segmentation and object classification. However, real-time lane line detection and departure estimates in complex traffic conditions have proven to be hard in autonomous driving research. Traditional lane line detection methods require manual parameter modification, but they have some limitations that are still susceptible to interference from obscuring objects, lighting changes, and pavement deterioration. The development of accurate lane line detection and departure estimate algorithms is still a challenge. This article investigated a convolutional neural network (CNN) for lane line detection and departure estimate in a complicated road environment. CNN includes a weight-sharing function that lowers the training parameters. CNN can learn and extract features frequently in image segmentation, object detection, classification, and other applications. The symmetric kernel convolution of classical CNN is upgraded to the structure of asymmetric kernel convolution (AK-CNN) based on lane line detection and departure estimation features. It reduces the CNN network's computational load and improves the speed of lane line detection and departure estimates. The experiment was carried out on the CULane dataset. The lane line detection results have high accuracy in a complex environment by 80.3%. The detection speed is 84.5 fps, which enables real-time lane line detection. Deep learning has made tremendous advances in the domains of image segmentation and object classification. However, real-time lane line detection and departure estimates in complex traffic conditions have proven to be hard in autonomous driving research. Traditional lane line detection methods require manual parameter modification, but they have some limitations that are still susceptible to interference from obscuring objects, lighting changes, and pavement deterioration. The development of accurate lane line detection and departure estimate algorithms is still a challenge. This article investigated a convolutional neural network (CNN) for lane line detection and departure estimate in a complicated road environment. CNN includes a weight-sharing function that lowers the training parameters. CNN can learn and extract features frequently in image segmentation, object detection, classification, and other applications. The symmetric kernel convolution of classical CNN is upgraded to the structure of asymmetric kernel convolution (AK-CNN) based on lane line detection and departure estimation features. It reduces the CNN network's computational load and improves the speed of lane line detection and departure estimates. The experiment was carried out on the CULane dataset. The lane line detection results have high accuracy in a complex environment by 80.3%. The detection speed is 84.5 fps, which enables real-time lane line detection. |
| Author | Haris, Malik Wang, Xiaomin Hou, Jin |
| Author_xml | – sequence: 1 givenname: Malik orcidid: 0000-0002-6450-1715 surname: Haris fullname: Haris, Malik organization: School of Information Science and Technology, Southwest Jiaotong University, National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University – sequence: 2 givenname: Jin orcidid: 0000-0001-7438-5327 surname: Hou fullname: Hou, Jin email: jhou@swjtu.edu.cn organization: School of Information Science and Technology, Southwest Jiaotong University, National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University – sequence: 3 givenname: Xiaomin orcidid: 0000-0003-4934-4288 surname: Wang fullname: Wang, Xiaomin organization: School of Information Science and Technology, Southwest Jiaotong University, National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University |
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| Cites_doi | 10.12720/joace.3.3.258-264 10.1109/IMCEC51613.2021.9482067 10.1016/j.image.2021.116413 10.1007/s00371-021-02103-8 10.1007/s00371-014-0918-5 10.1109/CVPR46437.2021.01390 10.3390/s20174719 10.1109/CVPRW50498.2020.00511 10.1007/978-3-030-58523-5_42 10.1016/j.aap.2017.12.001 10.1007/s00371-021-02161-y 10.1109/ICCV48922.2021.00375 10.1109/ICCV.2019.00301 10.1109/CVPR46437.2021.00036 10.1109/TIP.2020.2982832 10.3390/ELECTRONICS10161932 10.1007/978-3-030-72073-5_14 10.1109/TITS.2012.2184756 10.1109/CVPR.2019.00902 10.1109/TPAMI.2017.2699184 10.1109/WACV.2017.90 10.1109/CVPRW.2016.12 10.1049/iet-its.2017.0143 10.1109/ICMA49215.2020.9233837 10.1109/ICoIAS.2018.8494031 10.1007/s00371-019-01724-4 10.1109/MITS.2012.2189969 10.1007/s00371-021-02196-1 10.1109/IVS.2012.6232168 10.24963/ijcai.2021/138 10.1109/IVS.2016.7535517 10.1109/IVS.2017.7995911 10.1109/ICSCAN.2019.8878706 10.1007/978-3-030-58555-6_41 10.1007/978-3-030-58586-0_17 10.1109/TITS.2021.3088488 10.1109/LGRS.2021.3098774 10.3390/electronics10091102 10.1609/aaai.v32i1.12301 10.1109/ICCV.2019.00110 10.1109/TITS.2019.2926042 10.1109/ITNEC.2017.8284972 10.1007/PL00013394 10.1109/TNNLS.2016.2522428 10.1109/ICCV.2019.00059 10.1109/TITS.2006.874707 10.1007/s00371-020-02033-x 10.1049/iet-ipr.2013.0371 10.1109/TITS.2006.869595 10.1007/978-3-319-12637-1_57 10.1016/j.neunet.2016.12.002 10.1109/TIP.2015.2475625 |
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| Keywords | Asymmetric kernel CNN (AK-CNN) Lane line detection Lane departure estimation CULane dataset Scale perception |
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| References | Kumawat, A. Panda, S.: A robust edge detection algorithm based on feature-based image registration (FBIR) using improved canny with fuzzy logic (ICWFL). Vis. Comput. 1–22 (2021) Xiong, Y., et al.: “Upsnet: a unified panoptic segmentation network. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019, pp. 8810–8818. https://doi.org/10.1109/CVPR.2019.00902 GuotianFANBoLIQinHANRihuaJGangQURobust lane detection and tracking based on machine visionZTE Commun.20211846977 TranNGlobal Status Report on Road Safety2018GenevaWorld Health Organization511 YeYYHaoXLChenHJLane detection method based on lane structural analysis and CNNsIET Intel. Transport Syst.201812651352010.1049/iet-its.2017.0143 Kim, J., Lee, M.: Robust lane detection based on convolutional neural network and random sample consensus. Lecture Notes Computer Science (including Subseries in Lecture Notes Artificial Intelligence, Lecture Notes Bioinformatics), vol. 8834, pp. 454–461 (2014). https://doi.org/10.1007/978-3-319-12637-1_57 LiJMeiXProkhorovDTaoDDeep neural network for structural prediction and lane detection in traffic sceneIEEE Trans. Neural Netw. Learn. Syst.201628369070310.1109/TNNLS.2016.2522428 MammarSGlaserSNettoMTime to line crossing for lane departure avoidance: a theoretical study and an experimental settingIEEE Trans. Intell. Transp. Syst.20067222624110.1109/TITS.2006.874707 Su, J., Chen, C., Zhang, K., Luo, J., Wei, X., Wei, X.: Structure guided lane detection. arXiv Prepr. arXiv2105.05403 (2021) Xu, H., Wang, S., Cai, X., Zhang, W., Liang, X., Li, Z.: Curvelane-nas: unifying lane-sensitive architecture search and adaptive point blending. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XV 16, pp. 689–704 (2020) Choi, J., Chun, D., Kim, H., Lee, H.J.: Gaussian YOLOv3: an accurate and fast object detector using localization uncertainty for autonomous driving. In: Proceedings of the IEEE International Conference on Computer Vision, vol. 2019, pp. 502–511 (2019). https://doi.org/10.1109/ICCV.2019.00059 Gao, Q., Feng, Y., Wang, L.: A real-time lane detection and tracking algorithm. In: IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), pp. 1230–1234 (2017) Li, H.T., Todd, Z., Bielski, N., Carroll, F.: 3D lidar point-cloud projection operator and transfer machine learning for effective road surface features detection and segmentation. Vis. Comput. 1–16 (2021) WangXLiuYHaiDLane detection method based on double ROI and varied-line-spacing-scanningJ. Command Control201732154159 Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous distributed systems. Arxiv, 2016, [Online]. Available: http://arxiv.org/abs/1603.04467 McCallJCTrivediMMVideo-based lane estimation and tracking for driver assistance: survey, system, and evaluationIEEE Trans. Intell. Transp. Syst.200671203710.1109/TITS.2006.869595 Li, X., He, M., Li, H., Shen, H.: A combined loss-based multiscale fully convolutional network for high-resolution remote sensing image change detection. IEEE Geosci. Remote Sens. Lett. (2021) HarisMGlowaczALane line detection based on object feature distillationElectronics2021109110210.3390/electronics10091102 Yang, T., Liang, R., Huang, L.: Vehicle counting method based on attention mechanism SSD and state detection. Vis. Comput. 1–11 (2021) JeppssonHÖstlingMLubbeNReal life safety benefits of increasing brake deceleration in car-to-pedestrian accidents: simulation of vacuum emergency brakingAccid. Anal. Prev.201811131132010.1016/j.aap.2017.12.001 Wang, B., Wang, Z., Zhang, Y.: Polynomial regression network for variable-number lane detection. In: European Conference on Computer Vision, pp. 719–734 (2020) Tabelini, L., Berriel, R., Paixao, T.M., Badue, C., De Souza, A.F., Oliveira-Santos, T.: Keep your eyes on the lane: real-time attention-guided lane detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 294–302 (2021) Wang, Z., Ren, W., Qiu, Q.: LaneNet: real-time lane detection networks for autonomous driving. arXiv (2018) He, B., Ai, R., Yan, Y., Lang, X.: Accurate and robust lane detection based on Dual-View Convolutional Neutral Network. In: IEEE Intelligent Vehicles Symposium, Proceedings, vol. 2016, pp. 1041–1046. IEEE. https://doi.org/10.1109/IVS.2016.7535517 Qin, Z., Wang, H., Li, X.: Ultra fast structure-aware deep lane detection. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIV 16, pp. 276–291 (2020) Wen-juanGSYZYuan-juanTQZCombining the hough transform and an improved least squares method for line detectionComput. Sci.201244196200 ZhaoweiYUXiaoboWULinSIllumination invariant lane detection algorithm based on dynamic region of interestComput. Eng20174324356 GuillouEMeneveauxDMaiselEBouatouchKUsing vanishing points for camera calibration and coarse 3D reconstruction from a single imageVis. Comput.200016739641010.1007/PL000133941009.68976 DingLXuZZongJXiaoJShuCXuBA lane line detection algorithm based on convolutional neural networkGeom. Vis.2021138617510.1007/978-3-030-72073-5_14 An, F.-P., Liu, J., Bai, L.: Object recognition algorithm based on optimized nonlinear activation function-global convolutional neural network. Vis. Comput. 1–13 (2021) NCSA.: NCSA Data Resource Website, Fatality Analysis Reporting System (FARS) Encyclopaedia, p. 20. National Center for Statistics and Analysis (NCSA) Motor Vehicle Traffic Crash Data. US Department of Transportation. National Center for Statistics and Analysis (NCSA) Motor Vehicle Traffic Crash Data. US Department of Transportation (2018). Available: http://www-fars.nhtsa.dot.gov/main/index.aspx GopalanRHongTShneierMChellappaRA learning approach towards detection and tracking of lane markingsIEEE Trans. Intell. Transp. Syst.20121331088109810.1109/TITS.2012.2184756 ChenGHZhouWWangFJXiaoBJDaiSFLane detection based on improved canny detector and least square fittingAdv. Mater. Res.2013765–76723832387 Liu, L., Chen, X., Zhu, S., Tan, P.: CondLaneNet: a top-to-down lane detection framework based on conditional convolution. arXiv Prepr. arXiv2105.05003 (2021) Liu, S., Xiong, M., Zhong, W., Xiong, H.: Towards Industrial Scenario Lane Detection: Vision-Based AGV Navigation Methods. In: 2020 IEEE International Conference on Mechatronics and Automation, ICMA, pp. 1101–1106 (2020). https://doi.org/10.1109/ICMA49215.2020.9233837 HeZLiQFengHXuZFast and sub-pixel precision target tracking algorithm for intelligent dual-resolution cameraVis. Comput.20203661157117110.1007/s00371-019-01724-4 Zhao, K., Meuter, M., Nunn, C., Müller, D., Müller-Schneiders, S., Pauli, J.: A novel multi-lane detection and tracking system. In: IEEE Intelligent Vehicles Symposium, pp. 1084–1089 (2012) Chetlur, S., et al.: cuDNN: Efficient primitives for deep learning. arXiv, Oct. 2014, Accessed: Mar. 05, 2021. [Online]. Available: http://arxiv.org/abs/1410.0759 Singh, K., Seth, A., Sandhu, H.S., Samdani, K.: A comprehensive review of convolutional neural network based image enhancement techniques. In: IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), pp. 1–6 (2019) JiaBLiuRZhuMReal-time obstacle detection with motion features using monocular visionVis. Comput.201531328129310.1007/s00371-014-0918-5 Haris, M., Hou, J., Wang, X.: Multi-scale spatial convolution algorithm for lane line detection and lane offset estimation in complex road conditions. Signal Process. Image Commun. 116413 (2021) HarisMGlowaczARoad object detection: a comparative study of deep learning-based algorithmsElectronics20211016193210.3390/ELECTRONICS10161932 SrivastavaSLumbMSingalRLane detection using median filter, wiener filter and integrated hough transformJ. Autom. Control Eng.20153325826410.12720/joace.3.3.258-264 Yoo, S., et al.: End-to-end lane marker detection via row-wise classification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 1006–1007 (2020) Lee, H., Kim, S., Park, S., Jeong, Y., Lee, H., Yi, K.: AVM/LiDAR sensor based lane marking detection method for automated driving on complex urban roads. In: IEEE Intelligent Vehicles Symposium (IV), pp. 1434–1439 (2017) CuiGWangJLiJRobust multilane detection and tracking in urban scenarios based on LIDAR and mono-visionIET Image Process.20148526927910.1049/iet-ipr.2013.0371 Barsan, I.A., Wang, S., Pokrovsky, A., Urtasun, R.: Learning to localize using a lidar intensity map. arXiv Prepr. arXiv2012.10902 (2020) Bailo, O., Lee, S., Rameau, F., Yoon, J.S., Kweon, I.S.: Robust road marking detection & recognition using density-based grouping & machine learning techniques. In: Proceedings-2017 IEEE Winter Conference on Applications of Computer Vision, WACV 2017, pp. 760–768 (2017). https://doi.org/10.1109/WACV.2017.90 Zhu, J., Shi, F., Li, J.: Advanced driver assistance system based on machine vision. In: IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), vol. 4, pp. 2026–2030 (2021) Qu, Z., Jin, H., Zhou, Y., Yang, Z., Zhang, W.: Focus on local: detecting lane marker from bottom up via key point. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14122–14130 (2021) LiYHuangHLiXChenLNighttime lane markings detection based on Canny operator and Hough transformSci. Technol. Eng20161616711815 Hou, Y., Ma, Z., Liu, C., Loy, C.C.: Learning lightweight lane detection CNNS by self attention distillation. In: Proceedings of the IEEE International Conference on Computer Vision, vol. 2019, pp. 1013–1021. https://doi.org/10.1109/ICCV.2019.00110 GuoJKurupUShahMIs it safe to drive? An overview of factors, metrics, and datasets for driveability assessment in autonomous drivingIEEE Trans. Intell. Transp. 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| References_xml | – reference: Gurghian, A., Koduri, T., Bailur, S.V., Carey, K.J., Murali, V.N.: DeepLanes: end-to-end lane position estimation using deep neural networks. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 38–45 (2016). https://doi.org/10.1109/CVPRW.2016.12 – reference: WangXLiuYHaiDLane detection method based on double ROI and varied-line-spacing-scanningJ. Command Control201732154159 – reference: Wen-juanGSYZYuan-juanTQZCombining the hough transform and an improved least squares method for line detectionComput. Sci.201244196200 – reference: YeYYHaoXLChenHJLane detection method based on lane structural analysis and CNNsIET Intel. Transport Syst.201812651352010.1049/iet-its.2017.0143 – reference: GuotianFANBoLIQinHANRihuaJGangQURobust lane detection and tracking based on machine visionZTE Commun.20211846977 – reference: HarisMGlowaczARoad object detection: a comparative study of deep learning-based algorithmsElectronics20211016193210.3390/ELECTRONICS10161932 – reference: An, F.-P., Liu, J., Bai, L.: Object recognition algorithm based on optimized nonlinear activation function-global convolutional neural network. Vis. Comput. 1–13 (2021) – reference: Singh, K., Seth, A., Sandhu, H.S., Samdani, K.: A comprehensive review of convolutional neural network based image enhancement techniques. In: IEEE International Conference on System, Computation, Automation and Networking (ICSCAN), pp. 1–6 (2019) – reference: KimJKimJJangG-JLeeMFast learning method for convolutional neural networks using extreme learning machine and its application to lane detectionNeural Netw.20178710912110.1016/j.neunet.2016.12.002 – reference: Haris, M., Hou, J., Wang, X.: Multi-scale spatial convolution algorithm for lane line detection and lane offset estimation in complex road conditions. Signal Process. Image Commun. 116413 (2021) – reference: Liu, S., Xiong, M., Zhong, W., Xiong, H.: Towards Industrial Scenario Lane Detection: Vision-Based AGV Navigation Methods. In: 2020 IEEE International Conference on Mechatronics and Automation, ICMA, pp. 1101–1106 (2020). https://doi.org/10.1109/ICMA49215.2020.9233837 – reference: GuillouEMeneveauxDMaiselEBouatouchKUsing vanishing points for camera calibration and coarse 3D reconstruction from a single imageVis. Comput.200016739641010.1007/PL000133941009.68976 – reference: Zheng, T. et al.: Resa: recurrent feature-shift aggregator for lane detection. arXiv Prepr. arXiv2008.13719 (2020) – reference: CuiGWangJLiJRobust multilane detection and tracking in urban scenarios based on LIDAR and mono-visionIET Image Process.20148526927910.1049/iet-ipr.2013.0371 – reference: Qin, Z., Wang, H., Li, X.: Ultra fast structure-aware deep lane detection. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIV 16, pp. 276–291 (2020) – reference: LiYHuangHLiXChenLNighttime lane markings detection based on Canny operator and Hough transformSci. Technol. Eng20161616711815 – reference: ChanTHJiaKGaoSLuJZengZMaYPCANet: a simple deep learning baseline for image classification?IEEE Trans. Image Process.2015241250175032340609910.1109/TIP.2015.24756251408.94080 – reference: TarelJ-PHautiereNCaraffaLCordAHalmaouiHGruyerDVision enhancement in homogeneous and heterogeneous fogIEEE Intell. Transp. Syst. Mag.20124262010.1109/MITS.2012.2189969 – reference: Li, H.T., Todd, Z., Bielski, N., Carroll, F.: 3D lidar point-cloud projection operator and transfer machine learning for effective road surface features detection and segmentation. Vis. Comput. 1–16 (2021) – reference: Garnett, N., Cohen, R., Pe’Er, T., Lahav, R., Levi, D.: 3D-LaneNet: End-to-end 3D multiple lane detection. In: Proceedings of the IEEE International Conference on Computer Vision, vol. 2019, pp. 2921–2930. https://doi.org/10.1109/ICCV.2019.00301 – reference: Lee, H., Kim, S., Park, S., Jeong, Y., Lee, H., Yi, K.: AVM/LiDAR sensor based lane marking detection method for automated driving on complex urban roads. In: IEEE Intelligent Vehicles Symposium (IV), pp. 1434–1439 (2017) – reference: ChenLCPapandreouGKokkinosIMurphyKYuilleALDeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFsIEEE Trans. Pattern Anal. Mach. Intell.201840483484810.1109/TPAMI.2017.2699184 – reference: Yoo, S., et al.: End-to-end lane marker detection via row-wise classification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 1006–1007 (2020) – reference: GopalanRHongTShneierMChellappaRA learning approach towards detection and tracking of lane markingsIEEE Trans. Intell. Transp. Syst.20121331088109810.1109/TITS.2012.2184756 – reference: LiJMeiXProkhorovDTaoDDeep neural network for structural prediction and lane detection in traffic sceneIEEE Trans. Neural Netw. Learn. Syst.201628369070310.1109/TNNLS.2016.2522428 – reference: Kumawat, A. Panda, S.: A robust edge detection algorithm based on feature-based image registration (FBIR) using improved canny with fuzzy logic (ICWFL). Vis. Comput. 1–22 (2021) – reference: Pan, X., Shi, J., Luo, P., Wang, X., Tang, X.: Spatial as deep: spatial CNN for traffic scene understanding. In: 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, pp. 7276–7283 (2018) – reference: ZhaoweiYUXiaoboWULinSIllumination invariant lane detection algorithm based on dynamic region of interestComput. Eng20174324356 – reference: JiaBLiuRZhuMReal-time obstacle detection with motion features using monocular visionVis. Comput.201531328129310.1007/s00371-014-0918-5 – reference: Liang, D., et al.: “LineNet: a zoomable CNN for crowdsourced high definition maps modeling in urban environments. arXiv (2018) – reference: Liu, Y.-B., Zeng, M., Meng, Q.-H.: Heatmap-based vanishing point boosts lane detection. arXiv Prepr. arXiv2007.15602 (2020) – reference: Qu, Z., Jin, H., Zhou, Y., Yang, Z., Zhang, W.: Focus on local: detecting lane marker from bottom up via key point. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14122–14130 (2021) – reference: Xiong, Y., et al.: “Upsnet: a unified panoptic segmentation network. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2019, pp. 8810–8818. https://doi.org/10.1109/CVPR.2019.00902 – reference: TranNGlobal Status Report on Road Safety2018GenevaWorld Health Organization511 – reference: Barsan, I.A., Wang, S., Pokrovsky, A., Urtasun, R.: Learning to localize using a lidar intensity map. arXiv Prepr. arXiv2012.10902 (2020) – reference: HarisMHouJObstacle detection and safely navigate the autonomous vehicle from unexpected obstacles on the driving laneSensors (Switzerland)2020201712210.3390/s20174719 – reference: McCallJCTrivediMMVideo-based lane estimation and tracking for driver assistance: survey, system, and evaluationIEEE Trans. Intell. Transp. Syst.200671203710.1109/TITS.2006.869595 – reference: Zhu, J., Shi, F., Li, J.: Advanced driver assistance system based on machine vision. In: IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), vol. 4, pp. 2026–2030 (2021) – reference: Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous distributed systems. Arxiv, 2016, [Online]. Available: http://arxiv.org/abs/1603.04467 – reference: Gao, Q., Feng, Y., Wang, L.: A real-time lane detection and tracking algorithm. In: IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), pp. 1230–1234 (2017) – reference: Chetlur, S., et al.: cuDNN: Efficient primitives for deep learning. arXiv, Oct. 2014, Accessed: Mar. 05, 2021. [Online]. Available: http://arxiv.org/abs/1410.0759 – reference: He, B., Ai, R., Yan, Y., Lang, X.: Accurate and robust lane detection based on Dual-View Convolutional Neutral Network. In: IEEE Intelligent Vehicles Symposium, Proceedings, vol. 2016, pp. 1041–1046. IEEE. https://doi.org/10.1109/IVS.2016.7535517 – reference: HeZLiQFengHXuZFast and sub-pixel precision target tracking algorithm for intelligent dual-resolution cameraVis. Comput.20203661157117110.1007/s00371-019-01724-4 – reference: Tabelini, L., Berriel, R., Paixao, T.M., Badue, C., De Souza, A.F., Oliveira-Santos, T.: Keep your eyes on the lane: real-time attention-guided lane detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 294–302 (2021) – reference: JeppssonHÖstlingMLubbeNReal life safety benefits of increasing brake deceleration in car-to-pedestrian accidents: simulation of vacuum emergency brakingAccid. Anal. Prev.201811131132010.1016/j.aap.2017.12.001 – reference: Hou, Y., Ma, Z., Liu, C., Loy, C.C.: Learning lightweight lane detection CNNS by self attention distillation. In: Proceedings of the IEEE International Conference on Computer Vision, vol. 2019, pp. 1013–1021. https://doi.org/10.1109/ICCV.2019.00110 – reference: GuoJKurupUShahMIs it safe to drive? An overview of factors, metrics, and datasets for driveability assessment in autonomous drivingIEEE Trans. Intell. Transp. Syst.20192183135315110.1109/TITS.2019.2926042 – reference: ChenGHZhouWWangFJXiaoBJDaiSFLane detection based on improved canny detector and least square fittingAdv. Mater. Res.2013765–76723832387 – reference: Bailo, O., Lee, S., Rameau, F., Yoon, J.S., Kweon, I.S.: Robust road marking detection & recognition using density-based grouping & machine learning techniques. In: Proceedings-2017 IEEE Winter Conference on Applications of Computer Vision, WACV 2017, pp. 760–768 (2017). https://doi.org/10.1109/WACV.2017.90 – reference: Liu, L., Chen, X., Zhu, S., Tan, P.: CondLaneNet: a top-to-down lane detection framework based on conditional convolution. arXiv Prepr. arXiv2105.05003 (2021) – reference: DingLXuZZongJXiaoJShuCXuBA lane line detection algorithm based on convolutional neural networkGeom. Vis.2021138617510.1007/978-3-030-72073-5_14 – reference: Choi, J., Chun, D., Kim, H., Lee, H.J.: Gaussian YOLOv3: an accurate and fast object detector using localization uncertainty for autonomous driving. In: Proceedings of the IEEE International Conference on Computer Vision, vol. 2019, pp. 502–511 (2019). https://doi.org/10.1109/ICCV.2019.00059 – reference: Kim, J., Lee, M.: Robust lane detection based on convolutional neural network and random sample consensus. Lecture Notes Computer Science (including Subseries in Lecture Notes Artificial Intelligence, Lecture Notes Bioinformatics), vol. 8834, pp. 454–461 (2014). https://doi.org/10.1007/978-3-319-12637-1_57 – reference: Wang, B., Wang, Z., Zhang, Y.: Polynomial regression network for variable-number lane detection. In: European Conference on Computer Vision, pp. 719–734 (2020) – reference: Yang, T., Liang, R., Huang, L.: Vehicle counting method based on attention mechanism SSD and state detection. Vis. Comput. 1–11 (2021) – reference: Wang, Z., Ren, W., Qiu, Q.: LaneNet: real-time lane detection networks for autonomous driving. arXiv (2018) – reference: Ko, Y., Lee, Y., Azam, S., Munir, F., Jeon, M., Pedrycz, W.: Key points estimation and point instance segmentation approach for lane detection. IEEE Trans. Intell. Transp. Syst. (2021) – reference: NCSA.: NCSA Data Resource Website, Fatality Analysis Reporting System (FARS) Encyclopaedia, p. 20. National Center for Statistics and Analysis (NCSA) Motor Vehicle Traffic Crash Data. US Department of Transportation. National Center for Statistics and Analysis (NCSA) Motor Vehicle Traffic Crash Data. US Department of Transportation (2018). Available: http://www-fars.nhtsa.dot.gov/main/index.aspx – reference: Zhao, K., Meuter, M., Nunn, C., Müller, D., Müller-Schneiders, S., Pauli, J.: A novel multi-lane detection and tracking system. In: IEEE Intelligent Vehicles Symposium, pp. 1084–1089 (2012) – reference: MammarSGlaserSNettoMTime to line crossing for lane departure avoidance: a theoretical study and an experimental settingIEEE Trans. Intell. Transp. Syst.20067222624110.1109/TITS.2006.874707 – reference: HarisMGlowaczALane line detection based on object feature distillationElectronics2021109110210.3390/electronics10091102 – reference: ChenZShiJLiWLearned fast HEVC intra codingIEEE Trans. Image Process.2020295431544610.1109/TIP.2020.298283207586260 – reference: SrivastavaSLumbMSingalRLane detection using median filter, wiener filter and integrated hough transformJ. Autom. Control Eng.20153325826410.12720/joace.3.3.258-264 – reference: Li, X., He, M., Li, H., Shen, H.: A combined loss-based multiscale fully convolutional network for high-resolution remote sensing image change detection. IEEE Geosci. Remote Sens. Lett. (2021) – reference: Su, J., Chen, C., Zhang, K., Luo, J., Wei, X., Wei, X.: Structure guided lane detection. arXiv Prepr. arXiv2105.05403 (2021) – reference: Xu, H., Wang, S., Cai, X., Zhang, W., Liang, X., Li, Z.: Curvelane-nas: unifying lane-sensitive architecture search and adaptive point blending. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XV 16, pp. 689–704 (2020) – volume: 3 start-page: 258 issue: 3 year: 2015 ident: 2353_CR47 publication-title: J. Autom. Control Eng. doi: 10.12720/joace.3.3.258-264 – ident: 2353_CR8 doi: 10.1109/IMCEC51613.2021.9482067 – ident: 2353_CR10 doi: 10.1016/j.image.2021.116413 – ident: 2353_CR5 doi: 10.1007/s00371-021-02103-8 – volume: 31 start-page: 281 issue: 3 year: 2015 ident: 2353_CR37 publication-title: Vis. Comput. doi: 10.1007/s00371-014-0918-5 – ident: 2353_CR65 doi: 10.1109/CVPR46437.2021.01390 – volume: 43 start-page: 43 issue: 2 year: 2017 ident: 2353_CR17 publication-title: Comput. Eng – volume: 20 start-page: 1 issue: 17 year: 2020 ident: 2353_CR13 publication-title: Sensors (Switzerland) doi: 10.3390/s20174719 – ident: 2353_CR54 – ident: 2353_CR58 doi: 10.1109/CVPRW50498.2020.00511 – ident: 2353_CR61 doi: 10.1007/978-3-030-58523-5_42 – volume: 111 start-page: 311 year: 2018 ident: 2353_CR2 publication-title: Accid. Anal. Prev. doi: 10.1016/j.aap.2017.12.001 – ident: 2353_CR28 doi: 10.1007/s00371-021-02161-y – ident: 2353_CR66 doi: 10.1109/ICCV48922.2021.00375 – ident: 2353_CR43 doi: 10.1109/ICCV.2019.00301 – ident: 2353_CR63 doi: 10.1109/CVPR46437.2021.00036 – start-page: 5 volume-title: Global Status Report on Road Safety year: 2018 ident: 2353_CR1 – volume: 29 start-page: 5431 year: 2020 ident: 2353_CR46 publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2020.2982832 – volume: 10 start-page: 1932 issue: 16 year: 2021 ident: 2353_CR27 publication-title: Electronics doi: 10.3390/ELECTRONICS10161932 – volume: 1386 start-page: 175 year: 2021 ident: 2353_CR44 publication-title: Geom. Vis. doi: 10.1007/978-3-030-72073-5_14 – volume: 13 start-page: 1088 issue: 3 year: 2012 ident: 2353_CR22 publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2012.2184756 – ident: 2353_CR42 doi: 10.1109/CVPR.2019.00902 – volume: 40 start-page: 834 issue: 4 year: 2018 ident: 2353_CR55 publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2017.2699184 – volume: 765–767 start-page: 2383 year: 2013 ident: 2353_CR49 publication-title: Adv. Mater. Res. – volume: 4 start-page: 196 issue: 4 year: 2012 ident: 2353_CR48 publication-title: Comput. Sci. – ident: 2353_CR31 doi: 10.1109/WACV.2017.90 – ident: 2353_CR32 doi: 10.1109/CVPRW.2016.12 – volume: 12 start-page: 513 issue: 6 year: 2018 ident: 2353_CR45 publication-title: IET Intel. Transport Syst. doi: 10.1049/iet-its.2017.0143 – ident: 2353_CR53 – ident: 2353_CR19 – ident: 2353_CR30 doi: 10.1109/ICMA49215.2020.9233837 – ident: 2353_CR40 doi: 10.1109/ICoIAS.2018.8494031 – volume: 36 start-page: 1157 issue: 6 year: 2020 ident: 2353_CR6 publication-title: Vis. Comput. doi: 10.1007/s00371-019-01724-4 – volume: 4 start-page: 6 issue: 2 year: 2012 ident: 2353_CR52 publication-title: IEEE Intell. Transp. Syst. Mag. doi: 10.1109/MITS.2012.2189969 – ident: 2353_CR24 doi: 10.1007/s00371-021-02196-1 – volume: 16 start-page: 1671 year: 2016 ident: 2353_CR16 publication-title: Sci. Technol. Eng – ident: 2353_CR15 doi: 10.1109/IVS.2012.6232168 – ident: 2353_CR64 doi: 10.24963/ijcai.2021/138 – ident: 2353_CR25 doi: 10.1109/IVS.2016.7535517 – ident: 2353_CR20 doi: 10.1109/IVS.2017.7995911 – ident: 2353_CR11 doi: 10.1109/ICSCAN.2019.8878706 – ident: 2353_CR60 doi: 10.1007/978-3-030-58555-6_41 – ident: 2353_CR3 – volume: 3 start-page: 154 issue: 2 year: 2017 ident: 2353_CR18 publication-title: J. Command Control – ident: 2353_CR57 doi: 10.1007/978-3-030-58586-0_17 – ident: 2353_CR59 doi: 10.1109/TITS.2021.3088488 – ident: 2353_CR12 doi: 10.1109/LGRS.2021.3098774 – volume: 10 start-page: 1102 issue: 9 year: 2021 ident: 2353_CR39 publication-title: Electronics doi: 10.3390/electronics10091102 – ident: 2353_CR36 doi: 10.1609/aaai.v32i1.12301 – ident: 2353_CR56 – ident: 2353_CR41 – ident: 2353_CR62 – volume: 18 start-page: 69 issue: 4 year: 2021 ident: 2353_CR14 publication-title: ZTE Commun. – ident: 2353_CR38 doi: 10.1109/ICCV.2019.00110 – volume: 21 start-page: 3135 issue: 8 year: 2019 ident: 2353_CR51 publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2019.2926042 – ident: 2353_CR7 doi: 10.1109/ITNEC.2017.8284972 – volume: 16 start-page: 396 issue: 7 year: 2000 ident: 2353_CR34 publication-title: Vis. Comput. doi: 10.1007/PL00013394 – volume: 28 start-page: 690 issue: 3 year: 2016 ident: 2353_CR26 publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2016.2522428 – ident: 2353_CR29 doi: 10.1109/ICCV.2019.00059 – volume: 7 start-page: 226 issue: 2 year: 2006 ident: 2353_CR50 publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2006.874707 – ident: 2353_CR9 doi: 10.1007/s00371-020-02033-x – volume: 8 start-page: 269 issue: 5 year: 2014 ident: 2353_CR4 publication-title: IET Image Process. doi: 10.1049/iet-ipr.2013.0371 – volume: 7 start-page: 20 issue: 1 year: 2006 ident: 2353_CR35 publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2006.869595 – ident: 2353_CR23 doi: 10.1007/978-3-319-12637-1_57 – volume: 87 start-page: 109 year: 2017 ident: 2353_CR21 publication-title: Neural Netw. doi: 10.1016/j.neunet.2016.12.002 – volume: 24 start-page: 5017 issue: 12 year: 2015 ident: 2353_CR33 publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2015.2475625 |
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| SubjectTerms | Algorithms Artificial Intelligence Artificial neural networks Asymmetry Automobile safety Cameras Classification Computer Graphics Computer Science Deep learning Driving conditions Estimates Human error Image Processing and Computer Vision Image segmentation Machine learning Neural networks Object recognition Original Article Parameter modification Pavement deterioration Real time Roads & highways Sensors Teaching methods Traffic Traffic accidents & safety |
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