Technological measures of forefront road identification for vehicle comfort and safety improvement
Abstract
This paper presents the technological measures currently being developed at institutes and vehicle research centres dealing with forefront road identification. In this case, road identification corresponds with the surface irregularities and road surface type, which are evaluated by laser scanning and image analysis. Real-time adaptation, adaptation in advance and system external informing are stated as sequential generations of vehicle suspension and active braking systems where road identification is significantly important. Active and semi-active suspensions with their adaptation technologies for comfort and road holding characteristics are analysed. Also, an active braking system such as Anti-lock Braking System (ABS) and Autonomous Emergency Braking (AEB) have been considered as very sensitive to the road friction state. Artificial intelligence methods of deep learning have been presented as a promising image analysis method for classification of 12 different road surface types. Concluding the achieved benefit of road identification for traffic safety improvement is presented with reference to analysed research reports and assumptions made after the initial evaluation.
Keyword : road identification, road irregularities, laser scanning, semi-active suspension, damper, image analysis, friction state, deep learning, artificial intelligence, vehicle
How to Cite
Žuraulis, V., Surblys, V., & Šabanovič, E. (2019). Technological measures of forefront road identification for vehicle comfort and safety improvement. Transport, 34(3), 363-372. https://doi.org/10.3846/transport.2019.10372
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Nadav, I.; Katz, E. 2016. Off-road path and obstacle detection using monocular camera, in 2016 IEEE International Conference on the Science of Electrical Engineering (ICSEE), 16–18 November 2016, Eilat, Israel, 1–5. https://doi.org/10.1109/ICSEE.2016.7806132
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Van der Merwe, N. A.; Els, P. S.; Žuraulis, V. 2018. ABS braking on rough terrain, Journal of Terramechanics 80: 49–57. https://doi.org/10.1016/j.jterra.2018.10.003
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ZF Friedrichshafen AG. 2011. CDC® – Continuous Damping Control, ZF Friedrichshafen AG. Available from Internet: https://www.zf.com/products/en/cars/products_29310.html
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Zhang, X.; Xu, Y.; Pan, M.; Ren, F. 2014. A vehicle ABS adaptive sliding-mode control algorithm based on the vehicle velocity estimation and tyre/road friction coefficient estimations, Vehicle System Dynamics: International Journal of Vehicle Mechanics and Mobility 52(4): 475–503. https://doi.org/10.1080/00423114.2013.864775
Zheng, T.; Wang, L.; Ma, F. 2011. Research on road identification method in anti-lock braking system, Procedia Engineering 15: 194–198. https://doi.org/10.1016/j.proeng.2011.08.039
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Bauer, H. (Ed.). 1999. Driving-Safety Systems. Society of Automotive Engineers (SAE). 250 p.
Cafiso, S.; D’Agostino, C.; Delfino, E.; Montella, A. 2016. From manual to automatic pavement distress detection and classification, in 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), 26–28 June 2017, Naples, Italy, 433–438. https://doi.org/10.1109/MTITS.2017.8005711
Çalışkan, K.; Henze, R.; Küçükay, F. 2016. Potential of road preview for suspension control under transient road inputs, IFAC-PapersOnLine 49(3): 117–122. https://doi.org/10.1016/j.ifacol.2016.07.020
Cheng, G.; Zheng, J. Y.; Murase, H. 2018. Sparse coding of weather and illuminations for ADAS and autonomous driving, in 2018 IEEE Intelligent Vehicles Symposium (IV), 26–30 June 2018, Changshu, China, 2030–2035. https://doi.org/10.1109/IVS.2018.8500385
Dąbrowski, K.; Ślaski, G. 2016. Method and algorithm of automatic estimation of road surface type for variable damping control, IOP Conference Series: Materials Science and Engineering 148: 012028. https://doi.org/10.1088/1757-899X/148/1/012028
Dinçmen, E.; Acarman, T.; Aksun Güvenç, B. 2010. ABS control algorithm via extremum seeking method with enhanced lateral stability, IFAC Proceedings Volumes 43(7): 19–24. https://doi.org/10.3182/20100712-3-DE-2013.00017
Emam, A. S.; Abdel Ghany, A. M. 2012. Enhancement of ride quality of quarter vehicle model by using mixed H2/H with pole-placement, Engineering 4(2): 126–132. https://doi.org/10.4236/eng.2012.42016
Eneh, I. I.; Okafor, P. U. 2014. Design of an automatic brake control system using artificial neural network, International Journal of Scientific & Engineering Research 5(4): 1239–1245.
Eykholt, K.; Evtimov, I.; Fernandes, E.; Li, B.; Rahmati, A.; Xiao, C.; Prakash, A.; Kohno, T.; Song, D. 2018. Robust physical-world attacks on deep learning visual classification, in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 18–23 June 2018, Salt Lake City, UT, USA, 1625–1634. https://doi.org/10.1109/CVPR.2018.00175
Gerard, M.; Pasillas-Lépine, W.; De Vries, E.; Verhaegen, M. 2010. Adaptation of hybrid five-phase ABS algorithms for experimental validation, IFAC Proceedings Volumes 43(7): 13–18. https://doi.org/10.3182/20100712-3-DE-2013.00021
Gimonet, N.; Cord, A.; Saint Pierre, G. 2015. How to predict real road state from vehicle embedded camera?, in 2015 IEEE Intelligent Vehicles Symposium (IV), 28 June–1 July 2015, Seoul, South Korea, 593–598. https://doi.org/10.1109/IVS.2015.7225749
Göhrle, C.; Schindler, A.; Wagner, A.; Sawodny, O. 2015. Road profile estimation and preview control for low-bandwidth active suspension systems, IEEE/ASME Transactions on Mechatronics 20(5): 2299–2310. https://doi.org/10.1109/TMECH.2014.2375336
Ivanov, V.; Savitski, D.; Augsburg, K.; Barber, P.; Knauder, B.; Zehetner, J. 2015. Wheel slip control for all-wheel drive electric vehicle with compensation of road disturbances, Journal of Terramechanics 61: 1–10. https://doi.org/10.1016/j.jterra.2015.06.005
Kashem, S. B. A.; Chowdhury, M. A.; Choudhury, T. A.; Ektesabi, M.; Nagarajah, R. 2015. Study and review on vehicle suspension control theories and introduction of novel adaptive skyhook control system, Australian Journal of Basic and Applied Sciences 9(30): 1–12.
Koglbauer, I.; Holzinger, J.; Eichberger, A.; Lex, C. 2018. Autonomous emergency braking systems adapted to snowy road conditions improve drivers’ perceived safety and trust, Traffic Injury Prevention 19(3): 332–337. https://doi.org/10.1080/15389588.2017.1407411
Krasner, G.; Katz, E. 2016. Automatic parking identification and vehicle guidance with road awareness, in 2016 IEEE International Conference on the Science of Electrical Engineering (ICSEE), 16–18 November 2016, Eilat, Israel, 1–5. https://doi.org/10.1109/ICSEE.2016.7806133
Krauze, P.; Kasprzyk, J. 2016. Comparison of linear and nonlinear feedback control for a half-car model with MR dampers, in 2016 21st International Conference on Methods and Models in Automation and Robotics (MMAR), 29 August–1 September 2016, Miedzyzdroje, Poland, 965–970. https://doi.org/10.1109/MMAR.2016.7575268
Mahmud, F.; Arafat, A.; Zuhori, S. T. 2012. Intelligent autonomous vehicle navigated by using artificial neural network, in 2012 7th International Conference on Electrical and Computer Engineering, 20–22 December 2012, Dhaka, Bangladesh, 105–108. https://doi.org/10.1109/ICECE.2012.6471496
Marzbanrad, J.; Poozesh, P.; Damroodi, M. 2013. Improving vehicle ride comfort using an active and semi-active controller in a half-car model, Journal of Vibration and Control 19(9): 1357–1377. https://doi.org/10.1177/1077546312441814
Meignen, D.; Bernadet, M.; Briand, H. 1997. One application of neural networks for detection of defects using video data bases: identification of road distresses, in Database and Expert Systems Applications. 8th International Conference, DEXA’97. Proceedings, 1–2 September 1997, Toulouse, France, 459–464. https://doi.org/10.1109/DEXA.1997.617332
Menze, M.; Geiger, A. 2015. Object scene flow for autonomous vehicles, in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 7–12 June 2015, Boston, MA, USA, 3061–3070. https://doi.org/10.1109/CVPR.2015.7298925
Milz, S.; Arbeiter, G.; Witt, C.; Abdallah, B.; Yogamani, S. 2018. Visual SLAM for automated driving: exploring the applications of deep learning, in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 18–22 June 2018, Salt Lake City, UT, USA, 360–369. https://doi.org/10.1109/CVPRW.2018.00062
Mulla, A. A.; Unaune, D. R. 2013. Active suspensions future trend of automotive suspensions, in International Conference on Emerging Trends in Technology & its Applications (ICET-TA-2013), 6–7 March 2013, Karjat Mumbai, India, 1–9.
Nadav, I.; Katz, E. 2016. Off-road path and obstacle detection using monocular camera, in 2016 IEEE International Conference on the Science of Electrical Engineering (ICSEE), 16–18 November 2016, Eilat, Israel, 1–5. https://doi.org/10.1109/ICSEE.2016.7806132
Naguib, A. M.; Kim, J.; Lee, S. 2017. 3D environmental modeling and drivable road identification for a long-range rover, in 2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), 28 June–1 July 2017, Jeju, South Korea, 697–700. https://doi.org/10.1109/URAI.2017.7992802
Nwokah, O. D. I.; Hurmuzlu, Y. 2001. The Mechanical Systems Design Handbook: Modeling, Measurement, and Control. CRC Press. 872 p.
Oliveira, H.; Correia, P. L. 2008. Identifying and retrieving distress images from road pavement surveys, in 2008 15th IEEE International Conference on Image Processing, 12–15 October 2008, San Diego, CA, USA, 57–60. https://doi.org/10.1109/ICIP.2008.4711690
Pei, Q.; Na, J.; Huang, Y.; Gao, G.; Wu, X. 2016. Adaptive estimation and control of MR damper for semi-active suspension systems, in 2016 35th Chinese Control Conference (CCC), 27–29 July 2016, Chengdu, China, 3111–3116. https://doi.org/10.1109/ChiCC.2016.7553836
Pepe, G.; Carcaterra, A. 2016. VFC – variational feedback controller and its application to semi-active suspensions, Mechanical Systems and Signal Processing 76–77: 72–92. https://doi.org/10.1016/j.ymssp.2016.01.002
Prashanth, C.; Mala, J.; Santhosh, K. S.; Mithilesh, N. S. R. 2014. Road tracking using particle filters for Advanced Driver Assistance Systems, in 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), 8–11 October 2014, Qingdao, China, 1408–1414. https://doi.org/10.1109/ITSC.2014.6957884
Rao, Q.; Frtunikj, J. 2018. Deep learning for self-driving cars: chances and challenges, in SEFAIS’18: Proceedings of the 1st International Workshop on Software Engineering for AI in Autonomous Systems, 28 May 2018, Gothenburg, Sweden, 35–38. https://doi.org/10.1145/3194085.3194087
Rao, T. R. M.; Rao, G. V.; Rao, K. S.; Purushottam, A. 2010. Analysis of passive and semi active controlled suspension systems for ride comfort in an omnibus passing over a speed bump, International Journal of Research and Reviews in Applied Sciences 5(1): 7–17.
Sánchez-Torres, J. D.; Loukianov, A. G.; Galicia, M. I.; Rivera, J. 2011. A sliding mode regulator for antilock brake system, IFAC Proceedings Volumes 44(1): 7187–7192. https://doi.org/10.3182/20110828-6-IT-1002.03644
Savaresi, S.; Poussot-Vassal, C.; Spelta, C.; Sename, O.; Dugard, L. 2010. Semi-Active Suspension Control Design for Vehicles. Butterworth-Heinemann. 240 p.
Savaresi, S. M.; Spelta, C. 2009. A single-sensor control strategy for semi-active suspensions, IEEE Transactions on Control Systems Technology 17(1): 143–152. https://doi.org/10.1109/TCST.2008.906313
Shen, G. 2016. Road crack detection based on video image processing, in 2016 3rd International Conference on Systems and Informatics (ICSAI), 19–21 November 2016, Shanghai, China, 912–917. https://doi.org/10.1109/ICSAI.2016.7811081
Smolyanskiy, N.; Kamenev, A.; Birchfield, S. 2018. On the importance of stereo for accurate depth estimation: an efficient semi-supervised deep neural network approach, in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 18–22 June 2018, Salt Lake City, UT, USA, 1120–1127. https://doi.org/10.1109/CVPRW.2018.00147
Surblys, V.; Ślaski, G.; Pikosz, H. 2018. The usage of a laser height sensors for estimating road unevenness profile, The Archives of Automotive Engineering – Archiwum Motoryzacji 79(1): 95–106.
Tettamanti, T.; Varga, I.; Szalay, Z. 2016. Impacts of autonomous cars from a traffic engineering perspective, Periodica Polytechnica Transportation Engineering 44(4): 244–250. https://doi.org/10.3311/PPtr.9464
Udacity. 2016. Self-Driving Car: Datasets. GitHub, Inc. Available from Internet: https://github.com/udacity/self-driving-car/tree/master/datasets
Van der Merwe, N. A.; Els, P. S.; Žuraulis, V. 2018. ABS braking on rough terrain, Journal of Terramechanics 80: 49–57. https://doi.org/10.1016/j.jterra.2018.10.003
Van Hamme, D.; Veelaert, P.; Philips, W. 2013. Lane identification based on robust visual odometry, in 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), 6–9 October 2013, The Hague, The Netherlands, 1179–1183. https://doi.org/10.1109/ITSC.2013.6728392
Więckowski, D.; Dąbrowski, K.; Ślaski, G. 2018. Adjustable shock absorber characteristics testing and modelling, IOP Conference Series: Materials Science and Engineering 421: 022039. https://doi.org/10.1088/1757-899X/421/2/022039
Wong, J. Y. 2008. Theory of Ground Vehicles. Wiley, 592 p.
Yang, Z.; Zhang, Y.; Yu, J.; Cai, J.; Luo, J. 2018. End-to-end multi-modal multi-task vehicle control for self-driving cars with visual perceptions, in 2018 24th International Conference on Pattern Recognition (ICPR), 20–24 August 2018, Beijing, China, 2289–2294. https://doi.org/10.1109/ICPR.2018.8546189
Yi, J.; Alvarez, L.; Horowitz, R. 2002. Adaptive emergency braking control with underestimation of friction coefficient, IEEE Transactions on Control Systems Technology 10(3): 381–392. https://doi.org/10.1109/87.998027
ZF Friedrichshafen AG. 2011. CDC® – Continuous Damping Control, ZF Friedrichshafen AG. Available from Internet: https://www.zf.com/products/en/cars/products_29310.html
Zhang, L.; Wu, E.-Y. 2009. A road segmentation and road type identification approach based on new-type histogram calculation, in 2009 2nd International Congress on Image and Signal Processing, 17–19 October 2009, Tianjin, China, 1–5. https://doi.org/10.1109/CISP.2009.5300878
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