High-precision laser monitoring system with enhanced non-uniform scanning for railway safety

Abstract

The intrusion of obstacles onto railway tracks presents a significant threat to train safety, characterized by sudden and unpredictable occurrences. With China leading the world in high-speed rail mileage, ensuring railway security is paramount. The current laser monitoring technologies suffer from high false alarm rates and unreliable intrusion identification. This study addresses these issues by investigating high-resolution laser monitoring technology for railway obstacles, focusing on key parameters such as monitoring range and resolution. We propose an enhanced non-uniform laser scanning method, developing a laser monitoring system that reduces the obstacle false alarm rate to 2.00%, significantly lower than the 20% standard (TJ/GW135-2015). This rate is the best record for laser monitoring systems on China Railway. Our system operates seamlessly in all weather conditions, providing superior accuracy, resolution, and identification efficiency. It is the only 3D LiDAR system certified by the China State Railway Group Co., Ltd. (Certificate No. [2023] 008). Over three years, our system has been deployed at numerous points along various lines managed by the China State Railway Group, accumulating a dataset of 300,000 observations. This extensive deployment has significantly enhanced railway safety. The development and implementation of our railway laser monitoring system represent a substantial advancement in railway safety technology. Its low false alarm rate (2.00%), high accuracy (20 cm × 20 cm × 20 cm), and robust performance in diverse conditions underscore its potential for widespread adoption, promising to enhance railway safety in China and internationally.

References

[1.] China’s high-speed rail operation mileage reaches 45,000 km, http://www.china-railway.com.cn/xwzx/zhxw/202401/t20240112_132652.html. Accessed 12th Jan 2024

[2.] Fanos AM, Pradhan B. A novel rockfall hazard assessment using laser scanning data and 3D modelling in GIS. CATENA, 2019, 172: 435-450

[3.] Mentani A, Govoni L, Gottardi G, et al.. A new approach to evaluate the effectiveness of rockfall barriers. Proc Eng, 2016, 158: 398-403

[4.] Lan H, Martin CD, Zhou C, et al.. Rockfall hazard analysis using LiDAR and spatial modeling. Geomorphology, 2010, 118(1–2): 213-223

[5.] Ryan AK, Hutchinson DJ, Lato MJ, et al.. Identifying rock slope failure precursors using LiDAR for transportation corridor hazard management. Eng Geol, 2015, 195: 93-103

[6.] Soilán M, Sánchez-Rodríguez A, del Río-Barral P, et al.. Review of laser scanning technologies and their applications for road and railway infrastructure monitoring. Infrastructures, 2019, 4(4): 58

[7.] Dunham L, Wartman J, Olsen MJ, et al.. Rockfall activity index (RAI): a lidar-derived, morphology-based method for hazard assessment. Eng Geol, 2017, 221: 184-192

[8.] Ferrari F, Giacomini A, Thoeni K, et al.. Qualitative evolving rockfall hazard assessment for highwalls. Int J Rock Mech Min Sci, 2017, 98: 88-101

[9.] Netti T, Castelli M, De Biagi V. Effect of the number of simulations on the accuracy of a rockfall analysis. Proc Eng, 2016, 158: 464-469

[10.] Piszczek M. Data fusion in application of image information. Acta Phys Polon Ser A, 2011, 120(4): 716

[11.] Nairat M, Voelz D. Performance characteristics of a scanning laser imaging system through atmospheric turbulence. Opt Eng, 2012, 51(10): 101708

[12.] Piszczek M, Kowalski M, Karol M, et al.. Laser photography device-spatial parameters of imaging. Acta Phys Polon Ser A, 2013, 124(3): 550

[13.] Christnacher F, Schertzer S, Metzger N, et al.. Influence of gating and of the gate shape on the penetration capacity of range-gated active imaging in scattering environments. Opt Expr, 2015, 23(26): 32897

[14.] Lutz Y, Bacher E, Schertzer S. Accumulation mode laser range-gated viewing in the eye-safe spectral region. Opt Laser Technol, 2017, 96: 1-6

[15.] Laurenzis M, Christnacher F, Monnin D. Long-range three-dimensional active imaging with super-resolution depth mapping. Opt Lett, 2007, 32(21): 3146-3148

[16.] Schilling BW, Barr DN, Templeton GC, et al.. Multiple-return laser radar for three-dimensional imaging through obscurations. Appl Opt, 2002, 41(15): 2791-2799

[17.] Zhang X, Yan H, Zhou Q. Overcoming the shot-noise limitation of three-dimensional active imaging. Opt Lett, 2011, 36(8): 1434-1436

[18.] Chua SY, Wang X, Guo N, et al.. Influence of target reflection on three-dimensional range gated reconstruction. Appl Opt, 2016, 55(24): 6588-6595

[19.] Li L, Wu L, Wang X, et al.. Gated viewing laser imaging with compressive sensing. Appl Opt, 2012, 51(14): 2706-2712

[20.] Luo H, Yuan X, Zeng Y. Range accuracy of photon heterodyne detection with laser pulse based on Geiger-mode APD. Opt Expr, 2013, 21(16): 18983-18993

[21.] Chua SY, Wang X, Guo N, et al.. Range compensation for accurate 3D imaging system. Appl Opt, 2015, 55(1): 153-158

[22.] Oh MS, Kong HJ, Kim TH, et al.. Development and analysis of a photon-counting three-dimensional imaging laser detection and ranging (LADAR) system. J Opt Soc Am A, 2011, 28(5): 759-765

[23.] Gézero L, Antunes C. Automated three-dimensional linear elements extraction from mobile LiDAR point clouds in railway environments. Infrastructures, 2019, 4(3): 46

[24.] Che E, Jung J, Olsen MJ. Object recognition, segmentation, and classification of mobile laser scanning point clouds: a state of the art review. Sensors, 2019, 19(4): 810

[25.] Yadav M, Singh AK, Lohani B. Extraction of road surface from mobile LiDAR data of complex road environment. Int J Remote Sens, 2017, 38(16): 4655-4682

[26.] Jung J, Che E, Olsen MJ, et al.. Efficient and robust lane marking extraction from mobile lidar point clouds. ISPRS J Photogram Remote Sens, 2019, 147: 1-18

[27.] Huang P, Cheng M, Chen Y, et al.. Traffic sign occlusion detection using mobile laser scanning point clouds. IEEE Trans Intell Transp Syst, 2017, 18(9): 2364-2376

[28.] Mroué A, Heddebaut M, Elbahhar F, et al.. Automatic radar target recognition of objects falling on railway tracks. Meas Sci Technol, 2012, 23(2): 25401-25410

[29.] Nan Z, Zhu G, Zhang X, et al.. A novel high-precision railway obstacle detection algorithm based on 3D LiDAR. Sensors, 2024, 24(10): 3148

[30.] Kuk C. Enhanced clustering method using 3 D laser range data for an autonomous vehicle. Recent Adv Electr Eng, 2014, 8(6): 209-218

[31.] Zhou Y, Tuzel O (2018) VoxelNet: end-to-end learning for point cloud based 3D object detection. In: 2018 IEEE/CVF conference on computer vision and pattern recognition. Salt Lake City, pp 4490–4499

[32.] Yang S, Yang Y, Zhang L, et al.. Real time measurement of subgrade settlement in high speed railways with a resolution of 025 mm using a laser imaging method. Lasers Eng, 2020, 45(1–3): 1-14

[33.] Nan Z, Zhu G, Zhang X, et al.. Development of a high-precision lidar system and improvement of key steps for railway obstacle detection algorithm. Remote Sens, 2024, 16(10): 1761

Funding

National Natural Science Foundation of China(62275244)