Current Approaches in Traffic Lane Detection: a minireview

• Autorzy: Aron Csato , Florin Mariasiu

Identyfikatory

DOI

10.14669/AM/190157

• Języki publikacji: EN

Abstrakty

EN

The continuous development and importance of the field of road transport these days make it necessary to design, develop and implement technological solutions that reduce (eliminate as much as possible) the risk of road accidents. Such a technological solution is also represented by advanced driver assistance systems (ADAS), systems that assist drivers in various ways, such as collision avoidance, automatic parking, adaptive cruise control, attention and lane departure warnings. Over the next ten years, there will likely be a rise in the need for ADAS system deployment in automobile construction, driven by consumer and regulatory interest in safety applications that protect drivers and lower accident rates. At the moment, autonomous emergency braking and forward collision warning systems are mandated for all cars in the US and the EU. Additionally, advanced driver assistance systems (ADAS) may soon distinguish automobile brands and have a significant impact on consumer preference. The present work aims to provide a general picture related to the current research and development of ADAS systems that refer to the detection of the traffic lane and lane markings. The approaches are presented regarding: the current development directions of ADAS systems, current traffic lane detection techniques, traffic lane detection methods and the use of artificial intelligence techniques in this field. The general conclusion is that further research is needed in the field, research to increase the performance of traffic lane detective systems by using advanced algorithms and easy-to-implement methods that do not require large hardware resources.

Słowa kluczowe

EN

ADAS; autonomous driving technology; traffic lane detection; algorithm; artificial intelligence

• Wydawca: Wydawnictwo Naukowe Sieci Badawczej Łukasiewicz - Przemysłowego Instytutu Motoryzacji
• Czasopismo: Archiwum Motoryzacji
• Rocznik: 2024
• Tom: Vol. 104, nr 2
• Strony: 19--47
• Opis fizyczny: Bibliogr. 67 poz., rys.

Twórcy

autor

Aron Csato

• Automotive Engineering and Transportation, Technical University of Cluj-Napoca, Romania

autor

Florin Mariasiu

• EMARC Research Centre, Technical University of Cluj-Napoca, Romania

• https://orcid.org/0000-0003-4054-518X

Bibliografia

• [1] Holden E. Achieving Sustainable Mobility: Every day and Leisure-time Travel in the EU Transport and Mobility. Routledge. New York, NY 10017, USA, 2016:1–3. https://doi.org/10.4324/9781315565491
• [2] Golias J, Yannis G, Antoniou C. Classification of driver-assistance systems according to their impact on road safety and traffic efficiency. Transport Reviews. 2002;22(2):179–196. https://doi.org/10.1080/01441640110091215
• [3] Gietelink OJ. Design and validation of advanced driver assistance systems. PhD thesis, TRAIL Research School, Delft, 2007. Available at: http://resolver.tudelft.nl/uuid:b2f0e7f6-6255-4932-8b5e-d3ef67cd81ec (accessed on 09 Aug 2023)
• [4] Abdelgawad K, Abdelkarim M, Hassan B, Grafe M, Gräßler I. A Scalable Framework for Advanced Driver Assistance Systems Simulation. 6th International Conference on Advances in System Simulation (SIMUL 2014), 2014:43–51. Available at: https://hni-old.uni-paderborn.de/en/publications/ publikationen/?tx_hnippview_pi1%5Bpublikation%5D=8734 (accessed on 09 Aug 2023)
• [5] ETSC Road Safety Performance Index (PIN). Available at: https://etsc.eu/projects/pin/.” (accessed on 09 Aug 2023)
• [6] Europe on the Move: Commission Takes Action for Clean, Competitive and Connected Mobility. Available at: https://ec.europa.eu/transport/modes/road/news/2017-05-31-europe-on-themove_en (accessed on 09 Aug 2023)
• [7] The European New Car Assessment Programme. Available at: https://www.euroncap.com/en (accessed on 09 Aug 2023)
• [8] RTAD Road Safety Annual Report 2019. Available at: irtad-road-safety-annual-report-2019.pdf (itf-oecd.org) (accessed on 09 Aug 2023)
• [9] Simon JH. Learning to drive with Advanced Driver Assistance Systems empirical studies of an online tutor and a personalised warning display on the effects of learnability and the acquisition of skill. PhD thesis, 2005. Available at: https://core.ac.uk/download/pdf/153227967.pdf (accessed on 10 Sept 2023)
• [10] Masello L, Castignani G, Sheehan B, Murphy F, McDonnell K. On the road safety benefits of advanced driver assistance systems in different driving contexts. Transportation Research Interdisciplinary Perspectives. 2022;15:100670. https://doi.org/10.1016/j.trip.2022.100670
• [11] Teoh ER. Effectiveness of Antilock Braking Systems in Reducing Motorcycle Fatal Crash Rates. Traffic Injury Prevention. 2011;12(2):169–173. https://doi.org/10.1080/15389588.2010.541308
• [12] Young MS. Ergonomics issues with advanced driver assistance systems (ADAS). Automotive Ergonomics: Driver-Vehicle Interaction. CRC Press, 2016:55–76.
• [13] Ziębiński A, Cupek R, Grzechca D, Chruszczyk L. Review of advanced driver assistance systems (ADAS). Proceedings of the international conference of computational methods in sciences and engineering. AIP Conference Proceedings. 2017;1906:120002. https://doi.org/10.1063/1.5012394
• [14] Liu W, Li Z, Li L, Wang F. Parking Like a Human: A Direct Trajectory Planning Solution. IEEE Transactions on Intelligent Transportation Systems. 2017;18(12):3388–3397. https://doi.org/10.1109/TITS.2017.2687047
• [15] Cahyadi EF, Hwang MS. An improved efficient anonymous authentication with conditional privacy-preserving scheme for VANETs. PLoS One. 2021;16(9):e0257044. https://doi.org/10.1371/journal.pone.0257044
• [16] Wang Z. Research on Lane Detection Method Based on Machine Vision. Academic Journal of Engineering and Technology Science. 2023;6(8):37–43. https://doi.org/10.25236/AJETS.2023.060806
• [17] Muhammad K, Ullah A, Lloret J, Del Ser J, Albuquerque VHC. Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions. IEEE Transactions on Intelligent Transportation Systems. 2021;22(7):4316–4336. https://doi.org/10.1109/TITS.2020.3032227
• [18] Rateke T, Justen KA, Chiarella VF, Sobieranski AC, Comunello E, von Wangenheim A. Passive Vision Region-Based Road Detection. ACM Computing Surveys (CSUR). 2019;52:1–34. https://doi.org/10.1145/3311951
• [19] Pan J, Sun H, Xu K, Jiang Y, Xiao X, Hu J, Miao J. Lane-Attention: Predicting Vehicle Moving Trajectories by Learning Their Attention Over Lanes. IEEE International Conference on Intelligent Robots and Systems (IROS), Las Vegas, USA. 2020:7949–7956. https://doi.org/10.1109/IROS45743.2020.9341233
• [20] Rin V, Nuthong C. Front Moving Vehicle Detection and Tracking with Kalman Filter. IEEE 4th International Conference on Computer and Communication Systems (ICCCS), Singapore. 2019:304–310.https://doi.org/10.1109/CCOMS.2019.8821772
• [21] Cattaruzza M. Design and Simulation of Autonomous Driving Algorithms, PhD Thesis, 2019. Available at: https://webthesis.biblio.polito.it/11650/ (accessed on 10 Sept 2023)
• [22] Juang LH, Zhang JS. Robust visual line-following navigation system for humanoid robots. Artificial Intelligence Review. 2020;53(1):653–670. https://doi.org/10.1007/s10462-018-9672-9
• [23] Mancini A, Frontoni E, Zingaretti P. Mechatronic System to Help Visually Impaired Users During Walking and Running. IEEE Transactions on Intelligent Transportation Systems. 2018;19(2):649–660. https://doi.org/10.1109/TITS.2017.2780621
• [24] Xing Y, Lv C, Chen L, Wang H, Wang H, Cao D, et al. Advances in Vision-Based Lane Detection: Algorithms, Integration, Assessment, and Perspectives on ACP-Based Parallel Vision. IEEE/CAA Journal of Automatica Sinica. 2018;5(3):645–661. https://doi.org/10.1109/JAS.2018.7511063
• [25] Du X, Tan KK. Comprehensive and Practical Vision System for Self-Driving Vehicle Lane-Level Localization. IEEE Transactions on Image Processing. 2016;25(5):2075–2088. https://doi.org/10.1109/TIP.2016.2539683
• [26] Meuter M, Muller-Schneiders S, Mika A, Hold S, Nunn C, Kummert A. A Novel Approach to Lane Detection and Tracking. 12th International IEEE Conference on Intelligent Transportation Systems, St. Louis Missouri, United States. 2009:1–6. https://doi.org/10.1109/ITSC.2009.5309855
• [27] Veit T, Tarel JP, Nicolle P, Charbonnier P. Evaluation of Road Marking Feature Extraction. 11th International IEEE Conference on Intelligent Transportation Systems, Beijing, China. 2008:174–181. https://doi.org/10.1109/ITSC.2008.4732564
• [28] McCall JC, Trivedi MM. Video-based Lane estimation and tracking for driver assistance: survey, system, and evaluation. IEEE Transactions on Intelligent Transportation Systems. 2006;7(1):20–37.https://doi.org/10.1109/TITS.2006.869595
• [29] Dorj B, Lee D. A Precise Lane Detection Algorithm Based on Top View Image Transformation and Least-Square Approaches. Journal of Sensors. 2016:1–13. https://doi.org/10.1155/2016/4058093
• [30] Yeniaydin Y, Schmidt KW. Sensor Fusion of a Camera and 2D LIDAR for Lane Detection. 27th Signal Processing and Communications Applications Conference (SIU), Sivas, Turkey. 2019:1-4. https://doi.org/10.1109/SIU.2019.8806579
• [31] Fakhfakh M, Chaari L, Fakhfakh N. Bayesian curved lane estimation for autonomous driving. Journal of Ambient Intelligence and Humanized Computing. 2020;11:4133–4143. https://doi.org/10.1007/s12652-020-01688-7
• [32] Barmpounakis E, Sauvin GM, Geroliminis N. Lane Detection and Lane-Changing Identification with High-Resolution Data from a Swarm of Drones. Transportation Research Record: Journal of the Transportation Research Board. 2020;2674(7):1–15. https://doi.org/10.1177/0361198120920627
• [33] Aly M. Real time detection of lane markers in urban streets. IEEE Intelligent Vehicles Symposium.2008:7–12. https://doi.org/10.1109/IVS.2008.4621152
• [34] Zhou S, Jiang Y, Xi J, Gong J, Xiong G, Chen H. A novel lane detection based on geometrical model and Gabor filter. 2010 IEEE Intelligent Vehicles Symposium, La Jolla, CA, USA. 2010:59–64. https://doi.org/10.1109/IVS.2010.5548087
• [35] Wang Y, Shen D, Teoh EK. Lane detection using spline model. Pattern Recognition Letters.2000;21(8):677–689. https://doi.org/10.1016/S0167-8655(00)00021-0
• [36] Deng J, Han Y. A real-time system of lane detection and tracking based on optimized RANSAC B-spline fitting. Proceedings of the 2013 Research in Adaptive and Convergent Systems, RACS 2013. 2013:157–164. https://doi.org/10.1145/2513228.2513280
• [37] Li Q, Zhou J, Li B, Guo Y, Xiao J. Robust Lane-Detection Method for Low-Speed Environments. Sensors. 2018;18(12):4274. https://doi.org/10.3390/s18124274
• [38] Caraffi C, Cattani S, Grisleri P. Off-Road Path and Obstacle Detection Using Decision Networks and Stereo Vision. IEEE Transactions on Intelligent Transportation Systems. 2007;8(4):607–618. https://doi.org/10.1109/TITS.2007.908583
• [39] Tang J, Li S, Liu P. A review of lane detection methods based on deep learning. Pattern Recognition. 2021;111:107623. https://doi.org/10.1016/j.patcog.2020.107623
• [40] Sun Y, Wang L, Chen Y, Liu M. Accurate Lane Detection with Atrous Convolution and Spatial Pyramid Pooling for Autonomous Driving. IEEE International Conference on Robotics and Biomimetics (ROBIO). 2019:642–647. https://doi.org/10.1109/ROBIO49542.2019.8961705
• [41] Chetan NB, Gong J, Zhou H, Bi D, Lan J, Qie L. An overview of recent progress of lane detection for autonomous driving. In: 2019 6th International conference on dependable systems and their applications (DSA). IEEE. 2020:341–346. https://doi.org/10.1109/DSA.2019.00052
• [42] Borkar A, Hayes M, Smith MT. Polar randomized Hough transform for lane detection using loose constraints of parallel lines. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2011:1037–1040. https://doi.org/10.1109/ICASSP.2011.5946584
• [43] Son Y, Lee E, Kum D. Robust multi-lane detection and tracking using adaptive threshold and lane classification. Machine Vision and Applications. 2019;30(1):111–124. https://doi.org/10.1007/s00138-018-0977-0
• [44] Son J, Yoo H, Kim S, Sohn K. Real-time illumination invariant lane detection for lane departure warning system. Expert Systems with Applications. 2015;42(4):1816–1824. https://doi.org/10.1016/j.eswa.2014.10.024
• [45] Chen PR, Lo SY, Hang HM, Chan SW, Lin JJ. Efficient Road Lane Marking Detection with Deep Learning. IEEE 23rd International Conference on Digital Signal Processing, DSP 2018. 2018:8631673.https://doi.org/10.1109/ICDSP.2018.8631673
• [46] Lu Z, Xu Y, Shan X, Liu L, Wang X, Shen J. A Lane Detection Method Based on a Ridge Detector and Regional G-RANSAC. Sensors. 2019;19(18):4028. https://doi.org/10.3390/s19184028
• [47] Zheng F, Luo S, Song K, Yan C-W, Wang M-C. Improved Lane Line Detection Algorithm Based on Hough Transform. Pattern Recognition and Image Analysis. 2018;28(2):254–260. https://doi.org/10.1134/S1054661818020049
• [48] Kang C, Lee SH, Kee SC, Chung C. Kinematics-based Fault-tolerant Techniques: Lane Prediction for an Autonomous Lane Keeping System. International Journal of Control, Automation and Systems.2018;16(3):1293–1302. https://doi.org/10.1007/s12555-017-0449-8
• [49] Borkar A, Hayes M H, Smith MT. Robust Lane detection and tracking with Ransac and Kalman filter. In 16th IEEE International Conference on Image Processing (ICIP), Cairo, Egypt. 2009:3261–3264.https://doi.org/10.1109/ICIP.2009.5413980
• [50] Sun Y, Li J, Sun Z. Multi-Stage Hough Space Calculation for Lane Markings Detection via IMU and Vision Fusion. Sensors. 2019;19(10):2305. https://doi.org/10.3390/s19102305
• [51] Park H. Implementation of Lane Detection Algorithm for Self-driving Vehicles Using Tensor Flow. Advances in Intelligent Systems and Computing. 2019;773:438–447. https://doi.org/10.1007/978-3-319-93554-6_42
• [52] El Hajjouji I, Mars S, Asrih Z, El Mourabit A. A novel FPGA implementation of Hough Transform for straight lane detection. Engineering Science and Technology, an International Journal.2020;23(2):274–280. https://doi.org/10.1016/j.jestch.2019.05.008
• [53] Kim J, Lee M. Robust Lane Detection Based on Convolutional Neural Network and Random Sample Consensus. Lecture Notes in Computer Science. 2014;8834:454–461. https://doi.org/10.1007/978-3-319-12637-1_57
• [54] Gurghian A, Koduri T, Bailur SV, Carey KJ, Murali VN. DeepLanes: End-To-End Lane Position Estimation Using Deep Neural Networks. IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2016:38–45. https://doi.org/10.1109/CVPRW.2016.12
• [55] Zhang W, Mahale T. End to End Video Segmentation for Driving: Lane Detection for Autonomous Car. Deep Learning’ 18 Conference, Fall 2018, State College, PA, USA. 2018. https://doi.org/10.48550/arXiv.1812.05914
• [56] Pan X, Shi J, Luo P, Wang X, Tang X. Spatial as Deep: Spatial CNN for Traffic Scene Understanding. Proceedings of Thirty-Second AAAI Conference on Artificial Intelligence. 2018;32(1):7276–7283.https://doi.org/10.48550/arXiv.1712.06080
• [57] Ghafoorian M, Nugteren C, Baka N, Booij O, Hofmann M. EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection. Lecture Notes in Computer Science. 2018;11129:256–272. https://doi.org/10.1007/978-3-030-11009-3_15
• [58] Hinton G, Dean J, Vinyals O. Distilling the Knowledge in a Neural Network. NIPS 2014 Deep Learning Workshop. 2014. https://doi.org/10.48550/arXiv.1503.02531
• [59] Zagoruyko S, Komodakis N. Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer. 2017. https://doi.org/10.48550/arXiv.1612.03928
• [60] Philion J. FastDraw: Addressing the Long Tail of Lane Detection by Adapting a Sequential Prediction Network. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2019:11574–11583. https://doi.org/10.1109/CVPR.2019.01185
• [61] Wu Z, Qiu K, Yuan T, Chen H. A method to keep autonomous vehicles steadily drive based on lane detection. International Journal of Advanced Robotic Systems. 2021;18(2):1–11. https://doi.org/10.1177/17298814211002974
• [62] Dawam E, Feng X. Smart City Lane Detection for Autonomous Vehicle. 2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress.2020: 334-338, DOI: 10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00065
• [63] Muthalagu R, Bolimera A, Venkatesan K. Vehicle Lane markings segmentation and keypoint determination using deep convolutional neural networks. Multimedia Tools and Applications. 2021;80(7):11201–11215. https://doi.org/10.1007/s11042-020-10248-2
• [64] Yousri R, Elattar M, Darweesh M. A Deep Learning-Based Benchmarking Framework for Lane Segmentation in the Complex and Dynamic Road Scenes. IEEE Access. 2021;9:117565–117580.https://doi.org/10.1109/ACCESS.2021.3106377
• [65] Alajlan A, Almasri M. Automatic Lane marking prediction using convolutional neural network and S-Shaped Binary Butterfly Optimization. The Journal of Supercomputing. 2022;78(3):3715–3745.https://doi.org/10.1007/s11227-021-03988-x
• [66] Feng Z, Zhang S, Kunert M, Wiesbeck W. Applying Neural Networks with a High-Resolution Automotive Radar for Lane Detection. Proceeding sof AmE 2019 - Automotive meets Electronics; 10th GMM-Symposium. 2019:1–6
• [67] Pihlak R, Riid A. Simultaneous Road Edge and Road Surface Markings Detection Using Convolutional Neural Networks. Communications in Computer and Information Science. 2020;1243:109–121.https://doi.org/10.1007/978-3-030-57672-1_9