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Lane Marking Detection for Highway Scenes based on Solid-state LiDARs
Technical Paper
2021-01-7008
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Lane marking detection plays a crucial role in Autonomous Driving Systems or Advanced Driving Assistance System. Vision based lane marking detection technology has been well discussed and put into practical application. LiDAR is more stable for challenging environment compared to cameras, and with the development of LiDAR technology, price and lifetime are no longer an issue. We propose a lane marking detection algorithm based on solid-state LiDARs. First a series of data pre-processing operations were done for the solid-state LiDARs with small field of view, and the needed ground points are extracted by the RANSAC method. Then, based on the OTSU method, we propose an approach for extracting lane marking points using intensity information. According to the priori geometric features of lane markings, a series of post-processing algorithms are designed to further improve the performance and finally output accurate lane marking curve information, with detection range up to more than 100 meters. Our method is evaluated with the highway testing data collected by a self-driving heavy truck from Hong Jing Drive Company.
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Citation
Wu, W., Huang, L., Chen, S., Bai, J. et al., "Lane Marking Detection for Highway Scenes based on Solid-state LiDARs," SAE Technical Paper 2021-01-7008, 2021, https://doi.org/10.4271/2021-01-7008.Also In
References
- Chen , S. , Huang , L. , Chen , H. , and Bai , J. Multi-Lane Detection and Tracking Using Temporal-Spatial Model and Particle Filtering IEEE Transactions on Intelligent Transportation Systems. 2021 10.1109/TITS.2020.3035614
- Bertozzi , M. and Broggi , A. GOLD: A Parallel Real Time Stereo Vision System for Generic Obstacle and Lane Marking Detection IEEE Transactions on Image Processing. 7 1 1998 62 81 10.1109/83.650851
- Crisman , J.D. and Thorpe , C.E. SCARF: A Color Vision System that Tracks Roads and Intersections IEEE Transactions on Robotics and Automation. 9 1 1993 49 58 10.1109/70.210794
- Behringer , R. and Maurer , M. Results on Visual Road Recognition for Road Vehicle Guidance Proceedings of Conference on Intelligent Vehicles. 1996 415 420 10.1109/IVS.1996.566416
- Levinson , J. , Askeland , J. , Becker , J. , Dolson , J. et al. Towards Fully Autonomous Driving: Systems and Algorithms 2011 IEEE Intelligent Vehicles Symposium (IV) 163 168 2011 10.1109/IVS.2011.5940562
- Bry , A. , Bachrach , A. and Roy , N. State Estimation for Aggressive Flight in GPS-Denied Environments Using Onboard Sensing 2012 IEEE International Conference on Robotics and Automation 1 8 2012 10.1109/ICRA.2012.6225295
- Gao , F. , Wu , W. , Gao , W. , and Shen , S. Flying on point Clouds: Online Trajectory Generation and Autonomous Navigation for Quadrotors in Cluttered Environments Journal of Field Robotics. 36 4 2019 710 733 10.1002/rob.21842
- Nüchter , A. , Lingemann , K. , Hertzberg , J. , and Surmann , H. 6D SLAM—3D Mapping Outdoor Environments Journal of Field Robotics. 24 8-9 2007 699 722 10.1002/rob.20209
- Schwarz , B. Mapping the World in 3D Nature Photonics. 4 7 2010 429 430 10.1038/nphoton.2010.148
- Harris , M. Jan. 15, 2018 https://spectrum.ieee.org/cars-that-think/transportation/sensors/ces-2018-how-a-new-generation-lidars-is-redefining-the-car
- Lin , J. and Fu , Z. Loam Livox: A Fast, Robust, High-Precision Lidar Odometry and Mapping Package for Lidars of Small FoV 2020 IEEE International Conference on Robotics and Automation (ICRA) 3126 3131 2020 10.1109/ICRA40945.2020.9197440
- Ogawa , T. and Takagi , K. Lane Recognition Using On-vehicle LIDAR 2006 IEEE Intelligent Vehicles Symposium 540 545 2006 10.1109/IVS.2006.1689684
- Kibbel , J. , Justus , W. and Fürstenberg , K. Lane Marking Estimation and Departure Warning Using Multilayer Laserscanner 2005 IEEE Intelligent Transportation Systems Conference 607 611 2005 10.1109/ITSC.2005.1520147
- Lindner , P. , Richter , E. and Wanielik , G. Multi-Channel Lidar Processing for Lane Marking Detection and Estimation 2009 12th IEEE Conference on Intelligent Transportation Systems 1 6 2009 10.1109/ITSC.2009.5309704
- Choi , Y.W. , Jang , Y.W. , Lee , H.J. , and Cho , G.S. Three-Dimensional LiDAR Data Classifying to Extract Road Point in Urban Area IEEE Geoscience and Remote Sensing Letters. 5 4 2008 725 729 10.1109/LGRS.2008.2004470
- Li , T. and Deng , Z. A New 3D LIDAR-based Lane Markings Recognition Approach 2013 IEEE International Conference on Robotics and Biomimetics (ROBIO) 2197 2202 2013 10.1109/ROBIO.2013.6739795
- Hata , A , and Wolf , D. Road Marking Detection Using LIDAR Reflective Intensity Data and Its Application to Vehicle Localization 17th International IEEE Conference on Intelligent Transportation Systems (ITSC) 584 589 2014 10.1109/ITSC.2014.6957753
- Holgado-Barco , A. , Riveiro , B. , González-Aguilera , D. , and Arias , P. Automatic Inventory of Road Cross-Sections from Mobile Laser Scanning System Computer-Aided Civil and Infrastructure Engineering. 32 1 2017 3 17 10.1111/mice.12213
- Yang , B. , Fang , L. , Li , Q. , and Li , J. Automated Extraction of Road Markings from Mobile Lidar Point Clouds Photogrammetric Engineering & Remote Sensing. 78 4 2012 331 338 10.14358/PERS.78.4.331
- Zeybek , M. Extraction of Road Lane Markings from Mobile LiDAR Data Transportation Research Record: Journal of the Transportation Research Board. 2021 10.1177/0361198120981948
- Fischler , M.A. and Bolles , R.C. Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography Communications of the ACM. 24 6 1981 381 395 10.1145/358669.358692
- Urbančič , T. , Vrečko , A. , and Kregar , K. The Reliability of RANSAC Method when Estimating the Parameters of Geometric Object Geodetski Vestnik. 60 1 2016 69 97
- Otsu , N. A Threshold Selection Method from Gray-Level Histograms IEEE Transactions on Systems, Man and Cybernetics. 9 1 1979 62 66