Vision based road profile estimation for preview-controlled vehicle suspension systems

Yıl 2024, Cilt: 4 Sayı: 1, 30 – 40, 30.06.2024

Mert Büyükköprü Erdem Uzunsoy Xavier Mouton

https://doi.org/10.62189/ci.1266211

Öz

In this paper, a vision-based road profile estimation method was studied for the control of semi-active and active suspension systems. For the purpose, a monocular camera was used to collect data from the road tests to develop a logic to convert the camera measurements into the road profile data. For the generation of the road profile, alignment of the different sets of camera measurements and their coherence were expressed. Importance of the sensor and process noise removal were shown in recognition of the high frequency content of the road profile, which was a particular interest of the study. Additionally, a density-based clustering algorithm was taken into account to cluster the measured points vertically, to remove the process and sensor noise. The density-based clustering method reduced the noises and allowed detection of the high and low frequency contents of the road.

Anahtar Kelimeler

Clustering, Estimation, Road profile recognition

Destekleyen Kurum

Groupe Renault

Teşekkür

This work was supported by Groupe Renault S.A.S.

Kaynakça

• [1] Wong JY. Theory of Ground Vehicles. 3rd ed. Ottawa, Canada: Wiley; 2001.
• [2] Guerrero-Ibáñez J, Zeadally S, Contreras-Castillo J. Sensor Technologies for Intelligent Transportation Systems. Sensors, 2018;18:1212. DOI:10.3390/s18041212.
• [3] Ward CC, Iagnemma K. Speed-independent vibration-based terrain classification for passenger vehicles. 2009;47:1095–1113. DOI: 10.1080/00423110802450193.
• [4] Qin Y, Dong M, Zhao F, et al. Road profile classification for vehicle semi-active suspension system based on Adaptive Neuro-Fuzzy Inference System. Proc IEEE Conf Decis Control. 2015;54rd IEEE:1533–1538. DOI:10.1109/CDC.2015.7402428.
• [5] Qin Y, Langari R, Wang Z, et al. Road profile estimation for semi-active suspension using an adaptive Kalman filter and an adaptive super-twisting observer. Proc Am Control Conf. 2017;973–978. DOI:10.23919/ACC.2017.7963079.
• [6] Bender EK. Optimum Linear Preview Control With Application to Vehicle Suspension. J Basic Eng. 1968;90:213–221. DOI:10.1115/1.3605082.
• [7] Hac A. Optimal Linear Preview Control of Active Vehicle Suspension. Vehicle System Dynamics. 1992;21:167–195. DOI:10.1080/00423119208969008.
• [8] Oniga F, Nedevschi S, Meinecke MM, et al. Road surface and obstacle detection based on elevation maps from dense stereo. IEEE Conf Intell Transp Syst Proceedings, ITSC. 2007;859–865. DOI:10.1109/ITSC.2007.4357734.
• [9] Mehra RK, Amin JN, Hedrick KJ, et al. Active suspension using preview information and model predictive control. IEEE Conf Control Appl – Proc. 1997;860–865. DOI:10.1109/CCA.1997.627769.
• [10] Ryu S, Kim Y, Park Y. Robust H ∞ preview control of an active suspension system with norm-bounded uncertainties. Int J Automot Technol. 2008;9:585–592. DOI:10.1007/s12239-008-0069-7.
• [11] Tseng HE, Hrovat D. State of the art survey: active and semi-active suspension control. Vehicle System Dynamics. 2015;53:1034–1062. DOI:10.1080/00423114.2015.1037313.
• [12] Streiter R. Active preview suspension system. ATZ Worldw. 2008;110:4–11. DOI:10.1007/BF03225003.
• [13] Schindler A. New conception and first-time implementation of an active chassis with a preview strategy [Internet]. KIT Scientific Publishing; 2009 [cited 2024 Jan 15]. Available from: https://publikationen.bibliothek.kit.edu/1000013552.
• [14] Bouzouraa ME, Kellner M, Hofmann U, et al. Laser scanner based road surface estimation for automotive applications. Sensors 2014 IEEE. December 2014; Valencia, Spain. 2034–2037. DOI:10.1109/ICSENS.2014.6985434.
• [15] Gong M, Wang H, Wang X. Active Suspension Control Based on Estimated Road Class for Off-Road Vehicle. Math Probl Eng. 2019;2019. DOI:10.1155/2019/3483710.
• [16] Stein GP, Stein GP, Mano O, et al. A Robust Method for Computing Vehicle Ego-motion. IEEE Intell Veh Symp, Dearborn, MI, USA, 2000;362-368. DOI:10.1109/IVS.2000.898370.
• [17] Göhrle C, Schindler A, Wagner A, et al. Road Profile Estimation and Preview Control for Low-Bandwidth Active Suspension Systems. IEEE/ASME Trans Mechatronics. 2015;20:2299–2310. DOI:10.1109/TMECH.2014.2375336.
• [18] Weist U, Missel J, Cytrynski S, et al. Fahrkomfort der extraklasse. ATZextra. 2013;18:124–128. DOI: 10.1365/s35778-013-0060-4.
• [19] Shen T, Schamp G, Haddad M. Stereo vision based road surface preview. 17th IEEE Int Conf Intell Transp Syst ITSC 2014. Qingdao, China, 2014;1843–1849. DOI:10.1109/ITSC.2014.6957961.
• [20] Pfeiffer D, Gehrig S, Schneider N. Exploiting the Power of Stereo Confidences. 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, 2013;297-304. DOI:10.1109/CVPR.2013.45.
• [21] Hu X, Mordohai P. A quantitative evaluation of confidence measures for stereo vision. IEEE Trans Pattern Anal Mach Intell. 2012;34:2121–2133. DOI:10.1109/TPAMI.2012.46.
• [22] Suhr JK, Jung HG. Dense stereo-based robust vertical road profile estimation using hough transform and dynamic programming. IEEE Trans Intell Transp Syst. 2015;16:1528–1536. DOI:10.1109/TITS.2014.2369002.
• [23] Lee JK, Yoon KJ. Temporally Consistent Road Surface Profile Estimation Using Stereo Vision. IEEE Trans Intell Transp Syst. 2018;19:1618–1628. DOI:10.1109/TITS.2018.2794342.
• [24] Deigmoeller J, Einecke N, Fuchs O, et al. Road surface scanning using stereo cameras for motorcycles. VISIGRAPP 2018 – Proc 13th Int Jt Conf Comput Vision, Imaging Comput Graph Theory Appl. 2018;5:549–554. DOI:10.5220/0006614805490554
• [25] Schindler A, Göhrle C, Sawodny O. Method for precise scaling of an image of a camera sensor and system. European Patent EP2916102B1. 2019 Oct 23 [cited 2024 Jan 15]. Available from: https://data.epo.org/gpi/EP2916102B1-METHOD-FOR-PRECISE-SCALING-OF-AN-IMAGE-OF-A-CAMERA-SENSOR-AND-SYSTEM.
• [26] Sander J. Density-Based Clustering. Encyclopedia of Machine Learning. 2011;270–273. DOI:10.1007/978-0-387-30164-8_211.
• [27] Braune C, Besecke S, Kruse R. Density based clustering: Alternatives to DBSCAN. Partitional Clust Algorithms. Springer International Publishing. 2015;193–213. DOI:10.1007/978-3-319-09259-1_6.
• [28] Savaresi S, Poussot-Vassal C, Spelta C, et al. Semi-Active Suspension Control Design for Vehicles. 1st ed. Butterworth-Heinemann. 2010;71–90.
• [29] Shimoya N, Katsuyama E. A Study of Triple Skyhook Control for Semi-Active Suspension System. SAE Technical Paper 2019-01-0168. 2019. DOI:10.4271/2019-01-0168.
• [30] Büyükköprü M, Uzunsoy E, Mouton X. Yol Profili Kestirimi Yapılmasını Sağlayan Metot [Method That Enables Road Profile Estimation]. Türk Patent 2021 009190. 2023 Oct 23 [cited 2024 Jan 15]. Available from: https://portal.turkpatent.gov.tr/anonim/arastirma/patent/sonuc/dosya?patentAppNo=2021/009190&documentsTpye=all.
• [31] Ester M, Kriegel H-P, Sander J, et al. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. KDD'96: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. 1996;226-231.
• [32] Schubert E, Sander J, Ester M, et al. DBSCAN Revisited, Revisited: Why and How You Should (Still) Use DBSCAN. ACM Transactions on Database Systems. 2017;42:1-21. DOI:10.1145/3068335.

Yıl 2024, Cilt: 4 Sayı: 1, 30 – 40, 30.06.2024

Mert Büyükköprü Erdem Uzunsoy Xavier Mouton

https://doi.org/10.62189/ci.1266211

Öz

Kaynakça

• [1] Wong JY. Theory of Ground Vehicles. 3rd ed. Ottawa, Canada: Wiley; 2001.
• [2] Guerrero-Ibáñez J, Zeadally S, Contreras-Castillo J. Sensor Technologies for Intelligent Transportation Systems. Sensors, 2018;18:1212. DOI:10.3390/s18041212.
• [3] Ward CC, Iagnemma K. Speed-independent vibration-based terrain classification for passenger vehicles. 2009;47:1095–1113. DOI: 10.1080/00423110802450193.
• [4] Qin Y, Dong M, Zhao F, et al. Road profile classification for vehicle semi-active suspension system based on Adaptive Neuro-Fuzzy Inference System. Proc IEEE Conf Decis Control. 2015;54rd IEEE:1533–1538. DOI:10.1109/CDC.2015.7402428.
• [5] Qin Y, Langari R, Wang Z, et al. Road profile estimation for semi-active suspension using an adaptive Kalman filter and an adaptive super-twisting observer. Proc Am Control Conf. 2017;973–978. DOI:10.23919/ACC.2017.7963079.
• [6] Bender EK. Optimum Linear Preview Control With Application to Vehicle Suspension. J Basic Eng. 1968;90:213–221. DOI:10.1115/1.3605082.
• [7] Hac A. Optimal Linear Preview Control of Active Vehicle Suspension. Vehicle System Dynamics. 1992;21:167–195. DOI:10.1080/00423119208969008.
• [8] Oniga F, Nedevschi S, Meinecke MM, et al. Road surface and obstacle detection based on elevation maps from dense stereo. IEEE Conf Intell Transp Syst Proceedings, ITSC. 2007;859–865. DOI:10.1109/ITSC.2007.4357734.
• [9] Mehra RK, Amin JN, Hedrick KJ, et al. Active suspension using preview information and model predictive control. IEEE Conf Control Appl – Proc. 1997;860–865. DOI:10.1109/CCA.1997.627769.
• [10] Ryu S, Kim Y, Park Y. Robust H ∞ preview control of an active suspension system with norm-bounded uncertainties. Int J Automot Technol. 2008;9:585–592. DOI:10.1007/s12239-008-0069-7.
• [11] Tseng HE, Hrovat D. State of the art survey: active and semi-active suspension control. Vehicle System Dynamics. 2015;53:1034–1062. DOI:10.1080/00423114.2015.1037313.
• [12] Streiter R. Active preview suspension system. ATZ Worldw. 2008;110:4–11. DOI:10.1007/BF03225003.
• [13] Schindler A. New conception and first-time implementation of an active chassis with a preview strategy [Internet]. KIT Scientific Publishing; 2009 [cited 2024 Jan 15]. Available from: https://publikationen.bibliothek.kit.edu/1000013552.
• [14] Bouzouraa ME, Kellner M, Hofmann U, et al. Laser scanner based road surface estimation for automotive applications. Sensors 2014 IEEE. December 2014; Valencia, Spain. 2034–2037. DOI:10.1109/ICSENS.2014.6985434.
• [15] Gong M, Wang H, Wang X. Active Suspension Control Based on Estimated Road Class for Off-Road Vehicle. Math Probl Eng. 2019;2019. DOI:10.1155/2019/3483710.
• [16] Stein GP, Stein GP, Mano O, et al. A Robust Method for Computing Vehicle Ego-motion. IEEE Intell Veh Symp, Dearborn, MI, USA, 2000;362-368. DOI:10.1109/IVS.2000.898370.
• [17] Göhrle C, Schindler A, Wagner A, et al. Road Profile Estimation and Preview Control for Low-Bandwidth Active Suspension Systems. IEEE/ASME Trans Mechatronics. 2015;20:2299–2310. DOI:10.1109/TMECH.2014.2375336.
• [18] Weist U, Missel J, Cytrynski S, et al. Fahrkomfort der extraklasse. ATZextra. 2013;18:124–128. DOI: 10.1365/s35778-013-0060-4.
• [19] Shen T, Schamp G, Haddad M. Stereo vision based road surface preview. 17th IEEE Int Conf Intell Transp Syst ITSC 2014. Qingdao, China, 2014;1843–1849. DOI:10.1109/ITSC.2014.6957961.
• [20] Pfeiffer D, Gehrig S, Schneider N. Exploiting the Power of Stereo Confidences. 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, 2013;297-304. DOI:10.1109/CVPR.2013.45.
• [21] Hu X, Mordohai P. A quantitative evaluation of confidence measures for stereo vision. IEEE Trans Pattern Anal Mach Intell. 2012;34:2121–2133. DOI:10.1109/TPAMI.2012.46.
• [22] Suhr JK, Jung HG. Dense stereo-based robust vertical road profile estimation using hough transform and dynamic programming. IEEE Trans Intell Transp Syst. 2015;16:1528–1536. DOI:10.1109/TITS.2014.2369002.
• [23] Lee JK, Yoon KJ. Temporally Consistent Road Surface Profile Estimation Using Stereo Vision. IEEE Trans Intell Transp Syst. 2018;19:1618–1628. DOI:10.1109/TITS.2018.2794342.
• [24] Deigmoeller J, Einecke N, Fuchs O, et al. Road surface scanning using stereo cameras for motorcycles. VISIGRAPP 2018 – Proc 13th Int Jt Conf Comput Vision, Imaging Comput Graph Theory Appl. 2018;5:549–554. DOI:10.5220/0006614805490554
• [25] Schindler A, Göhrle C, Sawodny O. Method for precise scaling of an image of a camera sensor and system. European Patent EP2916102B1. 2019 Oct 23 [cited 2024 Jan 15]. Available from: https://data.epo.org/gpi/EP2916102B1-METHOD-FOR-PRECISE-SCALING-OF-AN-IMAGE-OF-A-CAMERA-SENSOR-AND-SYSTEM.
• [26] Sander J. Density-Based Clustering. Encyclopedia of Machine Learning. 2011;270–273. DOI:10.1007/978-0-387-30164-8_211.
• [27] Braune C, Besecke S, Kruse R. Density based clustering: Alternatives to DBSCAN. Partitional Clust Algorithms. Springer International Publishing. 2015;193–213. DOI:10.1007/978-3-319-09259-1_6.
• [28] Savaresi S, Poussot-Vassal C, Spelta C, et al. Semi-Active Suspension Control Design for Vehicles. 1st ed. Butterworth-Heinemann. 2010;71–90.
• [29] Shimoya N, Katsuyama E. A Study of Triple Skyhook Control for Semi-Active Suspension System. SAE Technical Paper 2019-01-0168. 2019. DOI:10.4271/2019-01-0168.
• [30] Büyükköprü M, Uzunsoy E, Mouton X. Yol Profili Kestirimi Yapılmasını Sağlayan Metot [Method That Enables Road Profile Estimation]. Türk Patent 2021 009190. 2023 Oct 23 [cited 2024 Jan 15]. Available from: https://portal.turkpatent.gov.tr/anonim/arastirma/patent/sonuc/dosya?patentAppNo=2021/009190&documentsTpye=all.
• [31] Ester M, Kriegel H-P, Sander J, et al. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. KDD'96: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. 1996;226-231.
• [32] Schubert E, Sander J, Ester M, et al. DBSCAN Revisited, Revisited: Why and How You Should (Still) Use DBSCAN. ACM Transactions on Database Systems. 2017;42:1-21. DOI:10.1145/3068335.

Toplam 32 adet kaynakça vardır.

Ayrıntılar

Kaynak Göster

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