The Empirical Application of Automotive 3D Radar Sensor for Target Detection for an Autonomous Surface Vehicle’s Navigation - Publikacja - MOST Wiedzy


The Empirical Application of Automotive 3D Radar Sensor for Target Detection for an Autonomous Surface Vehicle’s Navigation


Avoiding collisions with other objects is one of the most basic safety tasks undertaken in the operation of floating vehicles. Addressing this challenge is essential, especially during unmanned vehicle navigation processes in autonomous missions. This paper provides an empirical analysis of the surface target detection possibilities in a water environment, which can be used for the future development of tracking and anti-collision systems for autonomous surface vehicles (ASV). The research focuses on identifying the detection ranges and the field of view for various surface targets. Typical objects that could be met in the water environment were analyzed, including a boat and floating objects. This study describes the challenges of implementing automotive radar sensors for anti-collision tasks in a water environment from the perspective of target detection with the application for small ASV performing tasks on the lake.


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Informacje szczegółowe

Publikacja w czasopiśmie
artykuł w czasopiśmie wyróżnionym w JCR
Opublikowano w:
Remote Sensing nr 11, strony 1 - 18,
ISSN: 2072-4292
Rok wydania:
Opis bibliograficzny:
Stateczny A., Kazimierski W., Gronska-Sledz D., Motyl W.: The Empirical Application of Automotive 3D Radar Sensor for Target Detection for an Autonomous Surface Vehicle’s Navigation// Remote Sensing. -Vol. 11, iss. 10 (2019), s.1-18
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.3390/rs11101156
Bibliografia: test
  1. Droeschel, D.; Schwarz, M.; Behnke, S. Continuous mapping and localization for autonomous navigation in rough terrain using a 3D laser scanner. Robot. Auton. Syst. 2017, 88, 104-115. [CrossRef] otwiera się w nowej karcie
  2. Williams, G.M. Optimalization of eyesafe avalanche photodiode lidar for automobile safety and autonomous navigation systems. Opt. Eng. 2017, 56, 031224. [CrossRef] otwiera się w nowej karcie
  3. Huang, L.; Chen, S.; Zhang, J.; Cheng, B.; Liu, M. Real-Time Motion Tracking for Indoor Moving Sphere Objects with a LiDAR Sensor. Sensors 2017, 17, 1932. [CrossRef] [PubMed] otwiera się w nowej karcie
  4. Song, R.; Liu, Y.; Bucknall, R. Smoothed A* algorithm for practical unmanned surface vehicle path planning. Appl. Ocean Res. 2019, 83, 9-20. [CrossRef] otwiera się w nowej karcie
  5. Korayem, M.H.; Esfeden, R.A.; Nekoo, S.R. Path planning algorithm in wheeled mobile manipulators based on motion of Arms. J. Mech. Sci. Technol. 2015, 29, 1753-1763. [CrossRef] otwiera się w nowej karcie
  6. Maclachlan, R.; Mertz, C. Tracking of Moving Objects from a Moving Vehicle Using a Scanning Laser Rangefinder. In Proceedings of the 2006 IEEE Intelligent Transportation Systems Conference Proceedeings, Toronto, ON, Canada, 17-20 September 2006; pp. 301-306. [CrossRef] otwiera się w nowej karcie
  7. Wei, P.; Cagle, L.; Reza, T.; Ball, J.; Gafford, J. LiDAR and camera detection fusion in a real-time industrial multi-sensor collision avoidance system. Electronics 2018, 7, 6. [CrossRef] otwiera się w nowej karcie
  8. Polvara, R.; Sharma, S.; Wan, J.; Manning, A.; Sutton, R. Obstacle avoidance approaches for autonomous navigation of unmanned surface vehicles. J. Navig. 2017, 71. [CrossRef] otwiera się w nowej karcie
  9. Almeida, C.; Franco, T.; Ferreira, H.; Martins, A.; Santos, R.; Almeida, J.M.; Silva, E. Radar based collision detection developments on USV ROAZ II. In Proceedings of the Oceans 2009-Europe, Bremen, Germany, 11-14 May 2009; pp. 1-6. otwiera się w nowej karcie
  10. Zhuang, J.Y.; Zhang, L.; Zhao, S.Q.; Cao, J.; Wang, B.; Sun, H.B. Radar-based collision avoidance for unmanned surface vehicles. China Ocean Eng. 2016, 30, 867-883. [CrossRef] otwiera się w nowej karcie
  11. Eriksen, B.-O.H.; Wilthil, E.F.; Flåten, A.L.; Brekke, E.F.; Breivik, M. Radar-based Maritime Collision Avoidance using Dynamic Window. In Proceedings of the IEEE Aerospace Conference, Big Sky, MT, USA, 3-10 March 2018. [CrossRef] otwiera się w nowej karcie
  12. Gresham, I.; Jain, N.; Budka, T. A 76-77GHz Pulsed-Doppler Radar Module for Autonomous Cruise Control Applications. In Proceedings of the IEEE MTT-S International Microwave Symposium (IMS2000), Boston, MA, USA, 11-16 June 2000; pp. 1551-1554. otwiera się w nowej karcie
  13. Wolff, C. Frequency-Modulated Continuous-Wave Radar. Radar Tutorial. Available online: http://www. (accessed on 31 July 2018).
  14. Ramasubramanian, K. Using a Complex-Baseband Architecture in FMCW Radar Systems; Texas Instrument: Dallas, TX, USA, 2017; Available online: (accessed on 3 August 2018).
  15. Brückner, S. Maximum Length Sequences for Radar and Synchronization. Cuvillier. Available online: https: // (accessed on 2 August 2018).
  16. Automotive Radar-A Tale of Two Frequencies. life.augmented. Available online: automotive-radar-tale-two-frequencies/ (accessed on 3 August 2018). otwiera się w nowej karcie
  17. Sjöqvist, L.I. What Is an Automotive Radar? Gapwaves. Available online: is-an-automotive-radar (accessed on 3 August 2018).
  18. Schneider, M. Automotive Radar-Status and Trends. In Proceedings of the GeMiC 2005, Ulm, Germany, 5-7 April 2005; pp. 144-147.
  19. Ramasubramanian, K.; Ramaiah, K.; Aginsky, A. Moving from Legacy 24 GHz to State-Of-The-Art 77 GHz Radar; otwiera się w nowej karcie
  20. Texas Instrument: Dallas, TX, USA, 2017; Available online: (accessed on 3 August 2018). otwiera się w nowej karcie
  21. Matthews, A. What Is Driving the Automotive LiDAR and RADAR Market? Automotive Electronic Specifier, Kent, UK. 2017. Available online: automotive-lidar-and-radar-market#downloads (accessed on 3 August 2018).
  22. Bronzi, D.; Zou, Y.; Villa, F. Automotive Three-Dimensional Vision Through a Single-Photon Counting SPAD Camera. IEEE Trans. Intell. Transp. Syst. 2016, 17, 782-795. [CrossRef] otwiera się w nowej karcie
  23. Mende, R.; Rohling, H. New Automotive Applications for Smart Radar Systems; Smartmicro Publications: Braunshweig, Germany; Available online: (accessed on 3 August 2018).
  24. Jo, J.; Tsunoda, Y.; Stantic, B. A Likelihood-Based Data Fusion Model for the Integration of Multiple Sensor Data: A Case Study with Vision and Lidar Sensors. In Robot Intelligence Technology and Applications 4; otwiera się w nowej karcie
  25. Jooho, L.; W, W.J.; Nakwan, K. Obstacle Avoidance and Target Search of an Autonomous Surface Vehicle for 2016 Maritime RobotX Challenge. In Proceedings of the IEEE OES International Symposium on Underwater Technology (UT), Busan, Korea, 21-24 February 2017.
  26. Mei, J.H.; Arshad, M.R. COLREGs Based Navigation of Riverine Autonomous Surface Vehicle. In Proceedings of the IEEE 6TH International Conference on Underwater System Technology, Penang, Malaysia, 13-14 December 2016; pp. 145-149. otwiera się w nowej karcie
  27. Barton, A.; Volna, E. Control of Autonomous Robot using Neural Networks. In Proceedings of the International Conference on Numerical Analysis and Applied Mathematics 2016 (ICNAAM-2016), Rhodes, Greece, 19-25 September 2016; Volume 1863. otwiera się w nowej karcie
  28. Ko, B.; Choi, H.J.; Hong, C. Neural Network-based Autonomous Navigation for a Homecare Mobile Robot. In Proceedings of the 2017 IEEE International Conference on Big Data and Smart Computing (BIGCOMP), Jeju, Korea, 13-16 February 2017; pp. 403-406.
  29. Remote Sens. 2019, 11, 1156 otwiera się w nowej karcie
  30. Praczyk, T. Neural anti-collision system for Autonomous Surface Vehicle. Neurocomputing 2015, 149, 559-572. [CrossRef] otwiera się w nowej karcie
  31. Lil, J.; Bao, H.; Han, X. Real-time self-driving car navigation and obstacle avoidance using mobile 3D laser scanner and GNSS. Multimed. Tools Appl. 2016, 76, 23017-23039. otwiera się w nowej karcie
  32. Guan, R.P.; Ristic, B.; Wang, L.P. Feature-based robot navigation using a Doppler-azimuth radar. Int. J. Control 2017, 90, 888-900. [CrossRef] otwiera się w nowej karcie
  33. Guerrero, J.A.; Jaud, M.; Lenain, R. Towards LIDAR-RADAR based Terrain Mapping. In Proceedings of the 2015 IEEE International Workshop on Advanced Robotics and its Social Impacts (ARSO), Lyon, France, 30 June-2 July 2015. otwiera się w nowej karcie
  34. Hollinger, J.; Kutscher, B.; Close, B. Fusion of Lidar and Radar for detection of partially obscured objects. In Proceedings of the Unmanned Systems Technology XVII, Baltimore, MD, USA, 22 May 2015; Volume 9468. otwiera się w nowej karcie
  35. Mikhail, M.; Carmack, N. Navigation Software System Development for a Mobile Robot to Avoid Obstacles in a Dynamic Environment using Laser Sensor. In Proceedings of the SOUTHEASTCON 2017, Charlotte, NC, USA, 31 March-2 April 2017. otwiera się w nowej karcie
  36. Jeon, H.C.; Park, Y.B.; Park, C.G. Robust Performance of Terrain Referenced Navigation Using Flash Lidar. In Proceedings of the 2016 IEEE/Ion Position, Location and Navigation Symposium (PLANS), Savannah, GA, USA, 11-16 April 2016; pp. 970-975. otwiera się w nowej karcie
  37. Jiang, Z.; Wang, J.; Song, Q. Off-road obstacle sensing using synthetic aperture radar interferometry. J. Appl. Remote Sens. 2017, 11, 016010. [CrossRef] otwiera się w nowej karcie
  38. Oh, H.N.H.; Tsourdos, A.; Savvaris, A. Development of Collision Avoidance Algorithms for the C-Enduro USV. IFAC Proc. Vol. 2014, 47, 12174-12181. [CrossRef] otwiera się w nowej karcie
  39. Stateczny, A.; Kazimierski, W. Selection of GRNN network parameters for the needs of state vector estimation of manoeuvring target in ARPA devices. In Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments IV, Proceedings of the Society of Photo-Optical Instrumentation Engineers (SPIE), Wilga, Poland, 30 May-2 June 2005; otwiera się w nowej karcie
  40. Romaniuk, R.S., Ed.; SPIE: Bellingham, WA, USA, 2006; Volume 6159, p. F1591.
  41. Kazimierski, W.; Zaniewicz, G.; Stateczny, A. Verification of multiple model neural tracking filter with ship's radar. In Proceedings of the 13th International Radar Symposium (IRS), Warsaw, Poland, 23-25 May 2012; otwiera się w nowej karcie
  42. Xinchi, T.; Huajun, Z.; Wenwen, C.; Peimin, Z.; Zhiwen, L.; Kai, C. A Research on Intelligent Obstacle Avoidance for Unmanned Surface Vehicles. Proc. Chin. Autom. Congr. (CAC) 2018. [CrossRef] otwiera się w nowej karcie
  43. © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( otwiera się w nowej karcie
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