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Machine Learning Approach in Identifying Speed Breakers for Autonomous Driving: An Overview

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RITA 2018

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

Abstract

Advanced control systems for autonomous driving is capable of navigating vehicles without human interaction with appropriate devices by sensing the environment nearby the vehicle. Majority of such systems, autonomous vehicles implement a deliberative architecture that will pave the way for vehicle tracking, vehicle recognition, and collision avoidance. This paper provides a brief overview of the most advanced and recent approaches taken to detect and track speed breakers that employ various devices that allows pattern recognition. The discussion of various speed breaker detection will be limited to 3D reconstruction-based, vibration-based and vision-based. Moreover, the common machine learning models that have been used to investigate speed breakers are also discussed.

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References

  1. Romer C, Romer D (2013) Theory of machine learning 1–4

    Google Scholar 

  2. Tatsubori M, Walcott-Bryant A, Bryant R, Wamburu J (2018) A probabilistic hough transform for opportunistic crowd-sensing of moving traffic obstacles. https://epubs.siam.org/doi/10.1137/1.9781611975321.24

  3. Wong I. 8 types of speed bumps that annoy Malaysians|CARPUT. https://carput.my/malaysian-speed-bumps/

  4. Jain M, Singh A, Bali S, Kaul S (2012) Speed-breaker early warning system. In: Proceedings of the 6th USENIX/ACM work. Networked system device regulation

    Google Scholar 

  5. Govindarajulu P, Ezhumalai P (2018) In-vehicle intelligent transport system for preventing road accidents using internet of things. Int J Appl Eng Res 13:5661–5664

    Google Scholar 

  6. Afrin M, Mahmud MR, Razzaque MA (2016) Real time detection of speed breakers and warning system for on-road drivers. In: 2015 IEEE international WIE conference electrical and computer engineering WIECON-ECE 2015, pp 495–498

    Google Scholar 

  7. Chugh G, Bansal D, Sofat S (2014) Road condition detection using smartphone sensors: a survey. Int J Electron Electr Eng 7:595–602

    Google Scholar 

  8. González LC, Moreno R, Escalante HJ, Martinez F, Carlos MR (2017) Learning roadway surface disruption patterns using the bag of words representation. IEEE Trans Intell Transp Syst 18:2916–2928

    Article  Google Scholar 

  9. Moujahid A, Hina MD, Soukane A, Ortalda A (2018) Machine learning techniques in ADAS: a review, pp 235–242

    Google Scholar 

  10. Viswanathan V, Hussein R (2017) Applications of image processing and real-time embedded systems in autonomous cars: a short review. Int J Image Process 11:35

    Google Scholar 

  11. Byun J, Seo B-S, Lee J (2015) Toward accurate road detection in challenging environments using 3D point clouds. ETRI J 37:606–616

    Article  Google Scholar 

  12. Vo AV, Truong-Hong L, Laefer DF (2015) Aerial laser scanning and imagery data fusion for road detection in city scale. In: 2015 IEEE international geoscience and remote sensing symposium, pp 4177–4180

    Google Scholar 

  13. Carlos MR, Aragon ME, Gonzalez LC, Escalante HJ, Martinez F (2018) Evaluation of detection approaches for road anomalies based on accelerometer readings–addressing who’s who. IEEE Trans Intell Transp Syst 1–10

    Google Scholar 

  14. Tonde PVP, Jadhav A, Shinde S, Dhoka A, Bablade S (2015) Road quality and ghats complexity analysis using android sensors. Int J Adv Res Comput Commun Eng 4:101–104

    Article  Google Scholar 

  15. Priyanka Ramnath C (2015) Advanced driver assistance system (ADAS). Int J Adv Res Electron Commun Eng 4:2278–2909

    Google Scholar 

  16. Chen HT, Lai CY, Shih CA (2016) Toward community sensing of road anomalies using monocular vision. IEEE Sens J 16:2380–2388

    Article  Google Scholar 

  17. Gaisser F, Jonker PP (2017) Road user detection with convolutional neural networks: an application to the autonomous shuttle WEpod. In: Proceedings of 15th IAPR international conference on machine vision applications (MVA), pp 101–104

    Google Scholar 

  18. Melo S, Marchetti E, Cassidy S, Hoare E, Gashinova M, Cherniakov M (2018) 24 GHz interferometric radar for road hump detections in front of a vehicle. In: 2018 19th international radar symposium, pp 1–9

    Google Scholar 

  19. Lee M, Hur S, Park Y (2015) An obstacle classification method using multi-feature comparison based on 2D LIDAR database. In: Proceedings of the 12th international conference on information technology—new generations ITNG 2015, pp 674–679

    Google Scholar 

  20. Craciun D, Deschaud J-E, Goulette F (2017) Automatic Ground Surface Reconstruction from mobile laser systems for driving simulation engines. Simulation 93:201–211

    Article  Google Scholar 

  21. Masino J, Thumm J, Frey M, Gauterin F (2017) Learning from the crowd: road infrastructure monitoring system. J Traffic Transp Eng (English edn) 4:451–463

    Article  Google Scholar 

  22. Celaya-Padilla JM, Galván-Tejada CE, López-Monteagudo FE, Alonso-González O, Moreno-Báez A, Martínez-Torteya A, Galván-Tejada JI, Arceo-Olague JG, Luna-García H, Gamboa-Rosales H (2018) Speed bump detection using accelerometric features: a genetic algorithm approach. Sensors (Switzerland). 18:1–13

    Article  Google Scholar 

  23. Goregaonkar RK (2014) Assistance to driver and monitoring the accidents on road by using three axis accelerometer and GPS system. Int J Electron Commun Comput Eng 5:260–264

    Google Scholar 

  24. Reddy VK, Engineering RB (2016) Smart phone based speed breaker early warning system. Spec Issue Int J Comput Sci Inf Secur 14:20–25

    Google Scholar 

  25. Shelke V, Kalbhor T, Khanekar S, Shitole B, Kadam YV (2017) Study of estimation of road roughness condition and ghat complexity analysis using smartphone sensors

    Google Scholar 

  26. Dange T, Mahajan DV (2015) Analysis of road smoothness based on smartphones. Int J Innov Res Comput Commun Eng 3:5201–5206

    Article  Google Scholar 

  27. Mahajan DV, Dange T (2015) Estimation of road roughness condition by using sensors in smartphones. Int J Comput Eng Technol 6:41–49

    Google Scholar 

  28. Daraghmi Y-A, Daadoo M (2016) Intelligent smartphone based system for detecting speed bumps and reducing car speed. In: MATEC web conference, vol 77, p 09006

    Article  Google Scholar 

  29. Seraj F, van der Zwaag BJ, Dilo A, Luarasi T, Havinga P (2014) RoADS: a road pavement monitoring system for anomaly detection using smart phones. In: Proceedings of the 1st international work. Machine learning urban sensor data, SenseML, pp 1–16

    Google Scholar 

  30. Gónzalez LC, Martínez F, Carlos MR (2014) Identifying roadway surface disruptions based on accelerometer patterns. IEEE Lat Am Trans 12:455–461

    Article  Google Scholar 

  31. Devapriya W, Babu CNK, Srihari T (2016) Advance driver assistance system (ADAS)—speed bump detection. In: 2015 IEEE international conference on computational intelligence and computing research (ICCIC 2015)

    Google Scholar 

  32. Devapriya W, Babu CNK, Srihari T (2016) Real time speed bump detection using Gaussian filtering and connected component approach. In: IEEE WCTFTR 2016—proceedings of 2016 world conference futuristic trends in research and innovation for social welfare

    Google Scholar 

  33. Geetha Kiran A, Murali S (2014) Automatic hump detection and 3D view generation from a single road image. In: Proceedings of 2014 international conference on advances in computing, communications and informatics, ICACCI 2014, pp 2232–2238

    Google Scholar 

  34. Chen HT, Lai CY, Hsu CC, Lee SY, Lin BSP, Ho CP (2014) Vision-based road bump detection using a front-mounted car camcorder. In: Proceedings—international conference pattern recognition, pp 4537–4542

    Google Scholar 

  35. Jo Y, Ryu S (2015) Pothole detection system using a black-box camera. Sensors (Switzerland) 15:29316–29331

    Article  Google Scholar 

  36. Lee J, Yoon K (2018) Temporally consistent road surface profile estimation using stereo vision

    Article  Google Scholar 

  37. Himmelsbach M, von Hundelshausen F, Wuensche H (2010) Fast segmentation of 3D point clouds for ground vehicles. Iv, pp 560–565

    Google Scholar 

  38. Guo C, Sato W, Han L, Mita S, McAllester D (2011) Graph-based 2D road representation of 3D point clouds for intelligent vehicles. In: IEEE intelligent vehicles symposium proceedings, pp 715–721

    Google Scholar 

  39. Choi S, Park J, Byun J, Yu W (2014) Robust ground plane detection from 3D point clouds. In: Proceedings of 2014 14th international conference control, automation and systems (ICCAS 2014), pp 1076–1081 (2014)

    Google Scholar 

  40. Zhang Z, Huang K, Tan T (2006) Comparison of similarity measures for trajectory clustering in outdoor surveillance scenes. In: Proceedings—international conference on pattern recognition, vol 3, pp 1135–1138

    Google Scholar 

  41. Geiger A, Lenz P, Urtasun R. Are we ready for autonomous driving? The KITTI vision benchmark suite

    Google Scholar 

  42. Habermann D, Hata A, Wolf D, Osório FS (2013) Artificial neural nets object recognition for 3D point clouds, pp 101–106

    Google Scholar 

  43. Sun Y, Wang X, Tang X (2014) DeepID1: deep learning face representation from predicting 10000 classes. In: CVPR, pp 1891–1898

    Google Scholar 

  44. Chen D, Cao X, Wang L, Wen F, Sun J (2012) ECCV 2012 Bayesian. Dvi. pp 1–14

    Google Scholar 

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Acknowledgements

Universiti Malaysia Pahang fully supports the facilities and resources for this research. The authors would like to acknowledge the support of the internal grants of Universiti Malaysia Pahang (RDU1703159 and RDU180383).

Author information

Authors and Affiliations

  1. Innovative Manufacturing, Mechatronics and Sports Lab (iMAMS), Faculty of Manufacturing University Malaysia, Pahang, Malaysia

    Chun Sern Choong, Ahmad Fakhri Ab. Nasir, Anwar P. P. Abdul Majeed, Muhammad Aizzat Zakaria & Mohd Azraai Mohd Razman

Authors
  1. Chun Sern Choong
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  2. Ahmad Fakhri Ab. Nasir
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  3. Anwar P. P. Abdul Majeed
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  4. Muhammad Aizzat Zakaria
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  5. Mohd Azraai Mohd Razman
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Corresponding author

Correspondence to Ahmad Fakhri Ab. Nasir .

Editor information

Editors and Affiliations

  1. Faculty of Manufacturing Engineering, Universiti Malaysia Pahang, Pekan, Pahang Darul Makmur, Malaysia

    Anwar P. P. Abdul Majeed

  2. Faculty of Manufacturing Engineering, Universiti Malaysia Pahang, Pekan, Pahang Darul Makmur, Malaysia

    Jessnor Arif Mat-Jizat

  3. Faculty of Manufacturing Engineering, Universiti Malaysia Pahang, Pekan, Pahang Darul Makmur, Malaysia

    Mohd Hasnun Arif Hassan

  4. Faculty of Manufacturing Engineering, Universiti Malaysia Pahang, Pekan, Pahang Darul Makmur, Malaysia

    Zahari Taha

  5. Department of Aerospace Engineering, KAIST, Daejeon, Taejon-jikhalsi, Korea (Republic of)

    Han Lim Choi

  6. Department of Aerospace Engineering, KAIST, Daejeon, Taejon-jikhalsi, Korea (Republic of)

    Junmo Kim

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Choong, C.S., Ab. Nasir, A.F., P. P. Abdul Majeed, A., Zakaria, M.A., Mohd Razman, M.A. (2020). Machine Learning Approach in Identifying Speed Breakers for Autonomous Driving: An Overview. In: P. P. Abdul Majeed, A., Mat-Jizat, J., Hassan, M., Taha, Z., Choi, H., Kim, J. (eds) RITA 2018. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-13-8323-6_35

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