Survey on Lane Line Detection Techniques for Classifying Semantic Information Processing Modalities

doi:10.3778/j.issn.1002-8331.2406-0160

Abstract

Abstract: With the rapid development of autonomous driving technology, lane line detection, as its key component, has attracted widespread attention and shown great potential for application in intelligent transportation systems. However, traditional lane line detection techniques usually struggle to provide satisfactory recognition accuracy when dealing with complex environmental challenges. This paper reviews the development of lane detection technology and systematically sorts out 84 advanced algorithms, and innovatively divides them into four categories based on semantic processing: semantic segmentation assistance, semantic information fusion, semantic information enhancement, and semantic relationship mode-
ling. By deeply analyzing the technical characteristics and advantages of these algorithms, the main limitations of current lane line detection technology are revealed. Finally, the future development direction of lane line detection technology is put forward, especially in the utilization of semantic information, and the potential research direction is pointed out.

Key words: lane line detection, semantic information, autonomous driving, deep learning, computer vision

摘要： 随着自动驾驶技术的迅猛发展，车道线检测作为其关键组成部分，引起了广泛关注，并在智能交通系统中展现出巨大的应用潜力。然而，在应对复杂环境挑战时，传统车道线检测技术往往难以提供足够的识别精度。回顾车道线检测技术的发展轨迹，系统性地梳理了84种先进算法，并创新性地根据语义处理方式划分为四类别：语义分割辅助类、语义信息融合类、语义信息增强类和语义关系建模类。通过深入剖析算法的技术特点和优势，揭示了当前车道线检测技术所面临的主要局限。最后，对未来车道线检测技术的发展方向提出见解，特别是在语义信息利用方面，指出了潜在的研究方向。

关键词: 车道线检测, 语义信息, 自动驾驶, 深度学习, 计算机视觉

HONG Shuying, ZHANG Donglin. Survey on Lane Line Detection Techniques for Classifying Semantic Information Processing Modalities[J]. Computer Engineering and Applications, 2025, 61(5): 1-17.

洪书颖, 张东霖. 语义信息处理方式分类的车道线检测技术研究综述[J]. 计算机工程与应用, 2025, 61(5): 1-17.

References

[1] QIN Z Q, WANG H Y, LI X. Ultra fast structure-aware deep lane detection[C]//Proceedings of the IEEE International Conference on Computer Vision. Cham: Springer, 2020: 276-291.
[2] LEE S, KIM J, YOON J S, et al. VPGNet: vanishing point guided network for lane and road marking detection and recognition[C]//Proceedings of the 2017 IEEE International Conference on Computer Vision. Piscataway: IEEE, 2017: 1965-1973.
[3] ZHANG Y J, ZHU L, FENG W, et al. VIL-100: a new dataset and a baseline model for video instance lane detection[C]//Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2021: 15661-15670.
[4] CHEN L, SIMA C H, LI Y, et al. PersFormer: 3D lane detection viaPerspective transformer andtheOpenLane benchmark[C]//Proceedings of the IEEE International Conference on Computer Vision. Cham: Springer Nature Switzerland, 2022: 550-567.
[5] UDDIN N, HERMAWAN H, SITORUS F J P, et al. Lane detection system based on canny method for driving assistance[C]//Proceedings of the 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation. Piscataway: IEEE, 2023: 1-6.
[6] POKALE S, NITURE D V. On-board camera-based lane and traffic sign detection for 2 wheeler HeadLamp[C]//Proceedings of the 2022 IEEE 7th International Conference for Convergence in Technology (I2CT). Piscataway: IEEE, 2022: 1-6.
[7] YU G Q, QIU D. Research on lane detection method of intelligent vehicle in multi-road condition[C]//Proceedings of the 2021 China Automation Congress. Piscataway: IEEE, 2021: 2779-2783.
[8] AHMED Y A, MOHAMED A T, ALY A M B. Robust lane departure warning system for ADAS on highways[C]//Proceedings of the 2022 4th Novel Intelligent and Leading Emerging Sciences Conference. Piscataway: IEEE, 2022: 321-324.
[9] HASHEM A, SCHLECHTER T. Feature-based lane detection algorithms for track following: a comparative study[C]//Proceedings of the 2022 8th International Conference on Mechatronics and Robotics Engineering. Piscataway: IEEE, 2022: 169-173.
[10] SONG W J, YANG Y, FU M Y, et al. Lane detection and classification for forward collision warning system based on stereo vision[J]. IEEE Sensors Journal, 2018, 18(12): 5151-5163.
[11] SULTANA S, AHMED B. Lane detection and tracking under rainy weather challenges[C]//Proceedings of the 2021 IEEE Region 10 Symposium. Piscataway: IEEE, 2021: 1-6.
[12] HONG S, PARK D. Runtime virtual lane prediction based on inverse perspective transformation and machine learning for lane departure warning in low-power embedded systems[C]//Proceedings of the 2022 IEEE International Conference on Imaging Systems and Techniques. Piscataway: IEEE, 2022: 1-6.
[13] SWETHA S, SIVAKUMAR P. SSLA based traffic sign and lane detection for autonomous cars[C]//Proceedings of the 2021 International Conference on Artificial Intelligence and Smart Systems. Piscataway: IEEE, 2021: 766-771.
[14] RATH M K, SWAIN P K, BANERJEE S. An optimised deep learning approach of lane detection in complex Indian environment[C]//Proceedings of the 2022 1st IEEE International Conference on Industrial Electronics: Developments & Applications. Piscataway: IEEE, 2022: 1-5.
[15] FENG Z Y, GUO S H, TAN X, et al. Rethinking efficient lane detection via curve modeling[C]//Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2022: 17041-17049.
[16] CHNG Z M, LEW J M H, LEE J A. RONELD: robust neural network output enhancement for active lane detection[C]//Proceedings of the 2020 25th International Conference on Pattern Recognition. Piscataway: IEEE, 2021: 6842-6849.
[17] CHNG Z M, LEW J M H, LEE J A. RONELDv2: a faster, improved lane tracking method[J]. arXiv:2202.13137, 2022.
[18] WU P C, CHANG C, LIN C H. Lane-mark extraction for automobiles under complex conditions[J]. Pattern Recognition, 2014, 47(8): 2756-2767.
[19] MAMMERI A, BOUKERCHE A, TANG Z Z. A real-time lane marking localization, tracking and communication system[J]. Computer Communications, 2016, 73: 132-143.
[20] HOU Y N. Agnostic lane detection[J]. arXiv:1905.03704, 2019.
[21] ZHENG T, FANG H, ZHANG Y, et al. RESA: recurrent feature-shift aggregator for lane detection[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2021: 3547-3554.
[22] XU S H, CAI X Y, ZHAO B, et al. RCLane: relay chain prediction for lane detection[C]//Proceedings of the International Conference on Computer Vision. Cham: Springer Nature Switzerland, 2022: 461-477.
[23] CHEN Z Y, LIU Y, GONG M M, et al. Generating dynamic kernels via transformers for lane detection[C]//Proceedings of the 2023 IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2023: 6812-6821.
[24] BIN ABDUL RAZAK N, BIN MAZLAN M Z, JOHARI J B, et al. A lane detection using image processing technique for two-lane road[C]//Proceedings of the 2022 IEEE 10th Conference on Systems, Process & Control. Piscataway: IEEE, 2022: 214-219.
[25] RAHMAN Z, MORRIS B T. LVLane: deep learning for lane detection and classification in challenging conditions[C]//Proceedings of the 2023 IEEE 26th International Conference on Intelligent Transportation Systems. Piscataway: IEEE, 2023: 3901-3907.
[26] LIU L Z, CHEN X H, ZHU S Y, et al. CondLaneNet: a top-to-down lane detection framework based on conditional convolution[C]//Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2021: 3753-3762.
[27] ZHOU K Y, ZHOU R. End-to-end lane detection with one-to-several transformer[J]. arXiv:2305.00675, 2023.
[28] JAYASINGHE O, ANHETTIGAMA D, HEMACHANDRA S, et al. SwiftLane: towards fast and efficient lane detection[C]//Proceedings of the 2021 20th IEEE International Conference on Machine Learning and Applications. Piscataway: IEEE, 2021: 859-864.
[29] NEVEN D, DE BRABANDERE B, GEORGOULIS S, et al. Towards end-to-end lane detection: an instance segmentation approach[C]//Proceedings of the 2018 IEEE Intelligent Vehicles Symposium. Piscataway: IEEE, 2018: 286-291.
[30] CHE Q H, NGUYEN D P, PHAM M Q, et al. TwinLiteNet: an efficient and lightweight model for driveable area and lane segmentation in self-driving cars[C]//Proceedings of the 2023 International Conference on Multimedia Analysis and Pattern Recognition. Piscataway: IEEE, 2023: 1-6.
[31] XU H, WANG S J, CAI X Y, et al. CurveLane-NAS: unifying lane-sensitive architecture search and adaptive point blending[C]//Proceedings of the International Conference on Computer Vision. Cham: Springer, 2020: 689-704.
[32] ZHANG J Y, DENG T, YAN F, et al. Lane detection model based on spatio-temporal network with double convolutional gated recurrent units[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(7): 6666-6678.
[33] DONG Y Q, PATIL S, VAN AREM B, et al. A hybrid spatial-temporal deep learning architecture for lane detection[J]. Computer-Aided Civil and Infrastructure Engineering, 2023, 38(1): 67-86.
[34] JIN D, KIM D, KIM C S. Recursive video lane detection[C]//Proceedings of the 2023 IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2023: 8439-8448.
[35] XIE J, HAN J C, QI D Z, et al. Lane detection with position embedding[C]//Proceedings of the Fourteenth International Conference on Digital Image Processing, 2022: 78-86.
[36] LI C G, ZHANG B H, SHI J, et al. Multi-level domain adaptation for lane detection[C]//Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2022: 4379-4388.
[37] ZHENG T, HUANG Y F, LIU Y, et al. CLRNet: cross layer refinement network for lane detection[C]//Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2022: 888-897.
[38] ZHANG Y Z, ZHENG Y X, WU C Y, et al. ECPNet: an enhanced curve perception network for lane detection[C]//Proceedings of the 2024 IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway: IEEE, 2024: 7380-7384.
[39] ZHANG Q D, GAO Z L, ZHANG X L. A lane detection method based on multi-indicator scoring mechanism[C]//Proceedings of the 2023 4th International Conference on Computer Vision, Image and Deep Learning. Piscataway: IEEE, 2023: 613-616.
[40] LV Z N, HAN D, WANG W Z, et al. IFPNet: integrated feature pyramid network with fusion factor for lane detection[C]//Proceedings of the 2023 IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2023: 1880-1889.
[41] XING Y, LV C, CHEN L, et al. Advances in vision-based lane detection: algorithms, integration, assessment, and perspectives on ACP-based parallel vision[J]. IEEE/CAA Journal of Automatica Sinica, 2018, 5(3): 645-661.
[42] LUO Y, YAN X, ZHENG C, et al. M2-3DLaneNet: exploring multi-modal 3D lane detection[J]. arXiv:2209.05996, 2022.
[43] JUNG S, CHOI S, KHAN M A, et al. Towards lightweight lane detection by optimizing spatial embedding[J]. arXiv:2008.08311, 2020.
[44] CHENG W S, LUO H, YANG W, et al. Structure-aware network for lane marker extraction with dynamic vision sensor[J]. arXiv:2008.06204, 2020.
[45] TABELINI L, BERRIEL R, PAIX?O T M, et al. Keep your eyes on the lane: real-time attention-guided lane detection[C]//Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2021: 294-302.
[46] YANG J X, ZHANG L H, LU H C. Lane detection with versatile AtrousFormer and local semantic guidance[J]. Pattern Recognition, 2023, 133: 109053.
[47] LIU D Z, ZHU H, LI R, et al. LaneJoint: simultaneous lane detection and classification algorithm[C]//Proceedings of the 2022 China Automation Congress. Piscataway: IEEE, 2022: 2967-2972.
[48] XIAO L Y, LI X, YANG S, et al. ADNet: lane shape prediction via anchor decomposition[C]//Proceedings of the 2023 IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2023: 6381-6390.
[49] CHEN Z Y, SMITH-MILES K, DU B, et al. An efficient transformer for simultaneous learning of BEV and lane representations in 3D lane detection[J]. arXiv:2306.04927, 2023.
[50] YANG Z Y, SHEN C, SHAO W, et al. CANet: curved guide line network with adaptive decoder for lane detection[C]//Proceedings of the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway: IEEE, 2023: 1-5.
[51] GU Y C, MA C, LI Q, et al. 3D lane detection with attention in attention[J]. IEEE Signal Processing Letters, 2024, 31: 1104-1108.
[52] HOU Y N, MA Z, LIU C X, et al. Learning lightweight lane detection CNNs by self attention distillation[C]//Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2019: 1013-1021.
[53] HE Y, JIANG M Y, YE X Q, et al. Repainting and imitating learning for lane detection[C]//Proceedings of the 30th ACM International Conference on Multimedia. New York: ACM, 2022: 5508-5516.
[54] LEE M, LEE J, LEE D, et al. Robust lane detection via expanded self attention[C]//Proceedings of the 2022 IEEE/CVF Winter Conference on Applications of Computer Vision. Piscataway: IEEE, 2022: 1949-1958.
[55] MA L Y, ZHU H, DUAN H. A method of multiple lane detection based on constraints of lane information[C]//Proceedings of the 2021 China Automation Congress. Piscataway: IEEE, 2021: 4059-4064.
[56] QIU Q B, GAO H M, HUA W, et al. PriorLane: a prior knowledge enhanced lane detection approach based on transformer[C]//Proceedings of the 2023 IEEE International Conference on Robotics and Automation. Piscataway: IEEE, 2023: 5618-5624.
[57] FANG H Y, ZHU J, FANG Y. ContinuityLearner: geometric continuity feature learning for lane segmentation[J]. arXiv:2108.03507, 2021.
[58] KIM J, KIM J, JANG G J, et al. Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection[J]. Neural Networks, 2017, 87: 109-121.
[59] GAO X, BAI H L, XIONG Y J, et al. Robust lane line segmentation based on group feature enhancement[J]. Engineering Applications of Artificial Intelligence, 2023, 117: 105568.
[60] WANG L M, ZHONG H Y. FENet: focusing enhanced network for lane detection[C]//Proceedings of the 2024 IEEE International Conference on Multimedia and Expo. Piscataway: IEEE, 2024: 1-6.
[61] LIU R J, YUAN Z J, LIU T, et al. End-to-end lane shape prediction with transformers[C]//Proceedings of the 2021 IEEE Winter Conference on Applications of Computer Vision. Piscataway: IEEE, 2021: 3693-3701.
[62] LI C, SHI J, WANG Y, et al. Reconstruct from BEV: a 3D lane detection approach based on geometry structure prior[J]. arXiv:2206.10098, 2022.
[63] SU J, CHEN C, ZHANG K, et al. Structure guided lane detection[J]. arXiv:2105.05403, 2021.
[64] AI J Y, DING W B, ZHAO J H, et al. WS-3D-Lane: weakly supervised 3Dlane detection with 2D lane labels[C]//Proceedings of the 2023 IEEE International Conference on Robotics and Automation. Piscataway: IEEE, 2023: 5595-5601.
[65] WANG J S, MA Y C, HUANG S F, et al. A keypoint-based global association network for lane detection[C]//Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2022: 1382-1391.
[66] LUO Y R, ZHENG C D, YAN X, et al. LATR: 3Dlane detection from monocular images with transformer[C]//Proceedings of the 2023 IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2023: 7907-7918.
[67] ZHAO L Q, ZHENG W Z, ZHANG Y P, et al. StructLane: leveraging structural relations for lane detection[J]. IEEE Transactions on Image Processing, 2024, 33: 3692-3706.
[68] WANG R H, QIN J B, LI K, et al. BEV-LaneDet: a simple and effective 3D lane detection baseline[J]. arXiv:2210.06006, 2022.
[69] HUANG S F, SHEN Z W, HUANG Z H, et al. Anchor3DLane: learning to regress 3D anchors for monocular 3D lane detection[C]//Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2023: 17451-17460.
[70] REN H, WANG M W, LEI X Y, et al. Refinement bird’s eye view feature for 3D lane detection with dual-branch view transformation module[C]//Proceedings of the 2024 IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway: IEEE, 2024: 5930-5934.
[71] LI X, LI J, HU X L, et al. Line-CNN: end-to-end traffic line detection with line proposal unit[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(1): 248-258.
[72] BAI Y F, CHEN Z R, FU Z J, et al. CurveFormer: 3Dlane detection by curve propagation with curve queries and attention[C]//Proceedings of the 2023 IEEE International Conference on Robotics and Automation. Piscataway: IEEE, 2023: 7062-7068.
[73] HAN W, SHEN J. Decoupling the curve modeling and pavement regression for lane detection[J]. arXiv:2309.10533, 2023.
[74] EFRAT N, BLUVSTEIN M, ORON S, et al. 3D-LaneNet+: anchor free lane detection using a semi-local representation[J]. arXiv:2011.01535, 2020.
[75] LU Z Y, ZHANG H L, WANG R T, et al. Towards better 3D visual lane detection in traffic scenes via leveraging object semantics[C]//Proceedings of the 2023 IEEE 26th International Conference on Intelligent Transportation Systems. Piscataway: IEEE, 2023: 3817-3824.
[76] PAN X G, SHI J P, LUO P, et al. Spatial as deep: spatial CNN for traffic scene understanding[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2018: 7276-7283.
[77] QU Z, JIN H, ZHOU Y, et al. Focus on local: detecting lane marker from bottom up via key point[C]//Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2021: 14117-14125.
[78] ZHOU H Y, SONG X. Lane detection algorithm based on haar feature based coupled cascade classifier[C]//Proceedings of the 2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers. Piscataway: IEEE, 2021: 286-291.
[79] ABUALSAUD H, LIU S A, LU D B, et al. LaneAF: robust multi-lane detection with affinity fields[J]. IEEE Robotics and Automation Letters, 2021, 6(4): 7477-7484.
[80] JOY S, S M B, MUKESH T B, et al. Real time road lane detection using computer vision techniques in Python[C]//Proceedings of the 2022 International Conference on Automation, Computing and Renewable Systems. Piscataway: IEEE, 2022: 1228-1232.
[81] LI Z L, HAN C R, GE Z, et al. GroupLane: end-to-end 3D lane detection with channel-wise grouping[J]. IEEE Robotics and Automation Letters, 2024, 9(11): 10487-10494.
[82] MEYER A, SKUDLIK P, PAULS J H, et al. YOLinO: generic single shot polyline detection in real time[C]//Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision Workshops. Piscataway: IEEE, 2021: 2916-2925.
[83] JIN D, PARK W, JEONG S G, et al. Eigenlanes: data-driven lane descriptors for structurally diverse lanes[C]//Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2022: 17142-17150.
[84] SATO T, CHEN Q A. Towards driving-oriented metric for lane detection models[C]//Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2022: 17132-17141.
[85] 葛泽坤, 陶发展, 付主木, 等. 改进多头注意力机制的车道检测方法[J]. 计算机工程与应用, 2024, 60(2): 264-271.
GE Z K, TAO F Z, FU Z M, et al. Lane detection method based on improved multi-head self-attention[J]. Computer Engineering and Applications, 2024, 60(2): 264-271.
[86] 刘岩, 仇甜甜, 肖艳秋, 等. 融合改进GAN网络的夜视环境车道线检测[J]. 计算机工程与应用, 2023, 59（15）: 214-222.
LIU Y, QIUT T, XIAO Y Q, et al. Lane detection algorithm based on introduction of improved GAN network in night vision environment[J]. Computer Engineering and Applications, 2023, 59(15): 214-222.