{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T13:38:06Z","timestamp":1778852286244,"version":"3.51.4"},"reference-count":38,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,23]],"date-time":"2022-12-23T00:00:00Z","timestamp":1671753600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"German Federal Ministry of Transport and Digital Infrastructure (Bundesministerium f\u00fcr Verkehr und digitale Infrastruktur\u2014BMVI)","award":["01MM20012J"],"award-info":[{"award-number":["01MM20012J"]}]},{"name":"German Federal Ministry of Transport and Digital Infrastructure (Bundesministerium f\u00fcr Verkehr und digitale Infrastruktur\u2014BMVI)","award":["13FH7I08IA"],"award-info":[{"award-number":["13FH7I08IA"]}]},{"name":"Federal Ministry of Education and Research of Germany (Bundesministerium f\u00fcr Bildung und Forschung\u2014BMBF)","award":["01MM20012J"],"award-info":[{"award-number":["01MM20012J"]}]},{"name":"Federal Ministry of Education and Research of Germany (Bundesministerium f\u00fcr Bildung und Forschung\u2014BMBF)","award":["13FH7I08IA"],"award-info":[{"award-number":["13FH7I08IA"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>A highly accurate reference vehicle state is a requisite for the evaluation and validation of Autonomous Driving (AD) and Advanced Driver Assistance Systems (ADASs). This highly accurate vehicle state is usually obtained by means of Inertial Navigation Systems (INSs) that obtain position, velocity, and Course Over Ground (COG) correction data from Satellite Navigation (SatNav). However, SatNav is not always available, as is the case of roofed places, such as parking structures, tunnels, or urban canyons. This leads to a degradation over time of the estimated vehicle state. In the present paper, a methodology is proposed that consists on the use of a Machine Learning (ML)-method (Transformer Neural Network\u2014TNN) with the objective of generating highly accurate velocity correction data from On-Board Diagnostics (OBD) data. The TNN obtains OBD data as input and measurements from state-of-the-art reference sensors as a learning target. The results show that the TNN is able to infer the velocity over ground with a Mean Absolute Error (MAE) of 0.167\u00a0kmh (0.046\u00a0ms) when a database of 3,428,099 OBD measurements is considered. The accuracy decreases to 0.863\u00a0kmh (0.24\u00a0ms) when only 5000 OBD measurements are used. Given that the obtained accuracy closely resembles that of state-of-the-art reference sensors, it allows INSs to be provided with accurate velocity correction data. An inference time of less than 40 ms for the generation of new correction data is achieved, which suggests the possibility of online implementation. This supports a highly accurate estimation of the vehicle state for the evaluation and validation of AD and ADAS, even in SatNav-deprived environments.<\/jats:p>","DOI":"10.3390\/s23010159","type":"journal-article","created":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T02:55:07Z","timestamp":1672109707000},"page":"159","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Generation of Correction Data for Autonomous Driving by Means of Machine Learning and On-Board Diagnostics"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1615-7398","authenticated-orcid":false,"given":"Alberto","family":"Flores Fern\u00e1ndez","sequence":"first","affiliation":[{"name":"Technische Hochschule Ingolstadt, Esplanade 10, 85049 Ingolstadt, Germany"},{"name":"Escuela T\u00e9cnica Superior de Ingenier\u00eda Industrial, Universidad de Castilla-La Mancha, Calle Altagracia 50, 13001 Ciudad Real, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8622-0757","authenticated-orcid":false,"given":"Eduardo","family":"S\u00e1nchez Morales","sequence":"additional","affiliation":[{"name":"Technische Hochschule Ingolstadt, Esplanade 10, 85049 Ingolstadt, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0900-1697","authenticated-orcid":false,"given":"Michael","family":"Botsch","sequence":"additional","affiliation":[{"name":"Technische Hochschule Ingolstadt, Esplanade 10, 85049 Ingolstadt, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7762-9419","authenticated-orcid":false,"given":"Christian","family":"Facchi","sequence":"additional","affiliation":[{"name":"Technische Hochschule Ingolstadt, Esplanade 10, 85049 Ingolstadt, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0933-6291","authenticated-orcid":false,"given":"Andr\u00e9s","family":"Garc\u00eda Higuera","sequence":"additional","affiliation":[{"name":"Escuela T\u00e9cnica Superior de Ingenier\u00eda Industrial, Universidad de Castilla-La Mancha, Calle Altagracia 50, 13001 Ciudad Real, Spain"},{"name":"European Parliamentary Research Service, Rue Wiertz 60, B-1047 Brussels, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Flores Fern\u00e1ndez, A., Wurst, J., S\u00e1nchez Morales, E., Botsch, M., Facchi, C., and Garc\u00eda Higuera, A. (2022). Probabilistic Traffic Motion Labeling for Multi-Modal Vehicle Route Prediction. Sensors, 22.","DOI":"10.3390\/s22124498"},{"key":"ref_2","unstructured":"(2018, March 16). Release No: CR-048-17, Available online: https:\/\/www.defense.gov\/News\/Contracts\/Contract-View\/Article\/1112618\/."},{"key":"ref_3","unstructured":"Lam, K. (2018, March 16). Broadcom Introduces World\u2019s First Dual Frequency GNSS Receiver with Centimeter Accuracy for Consumer LBS Applications. Available online: https:\/\/www.broadcom.com\/company\/news\/product-releases\/2302120."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"El-Saigh, A.I., and Macario, R.C.V. (1996, January 23). A review of anti-multipath techiques, past and present. Proceedings of the IEE Colloquium on Multipath Countermeasures, London, UK.","DOI":"10.1049\/ic:19960755"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Burr, A.G. (1996, January 23). The multipath problem: An overview. Proceedings of the IEE Colloquium on Multipath Countermeasures, London, UK.","DOI":"10.1049\/ic:19960754"},{"key":"ref_6","unstructured":"Cheng, L., Chen, J., and Gan, M. (2010, January 29\u201331). Multipath error analysis of carrier Tracking Loop in GPS receiver. Proceedings of the 29th Chinese Control Conference, Beijing, China."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Liu, L., and Amin, M.G. (2007;, January 15\u201320). Comparison of Average Performance of GPS Discriminators in Multipath. Proceedings of the 2007 IEEE International Conference on Acoustics, Speech and Signal Processing\u2014ICASSP \u201907, Honolulu, HI, USA.","DOI":"10.1109\/ICASSP.2007.367079"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yedukondalu, K., Sarma, A.D., and Kumar, A. (2010, January 6\u20139). Mitigation of GPS multipath error using recursive least squares adaptive filtering. Proceedings of the 2010 IEEE Asia Pacific Conference on Circuits and Systems, Kuala Lumpur, Malaysia.","DOI":"10.1109\/APCCAS.2010.5775022"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1515\/jaiscr-2017-0017","article-title":"A Machine Learning Approach for the Segmentation of Driving Maneuvers and its Application in Autonomous Parking","volume":"7","author":"Notomista","year":"2017","journal-title":"J. Artif. Intell. Soft Comput. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18178\/ijmlc.2021.11.1.1007","article-title":"Sampling Algorithms Combination with Machine Learning for Efficient Safe Trajectory Planning","volume":"11","author":"Chaulwar","year":"2021","journal-title":"Int. J. Mach. Learn. Comput."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"S\u00e1nchez Morales, E., Dauth, J., Huber, B., Garc\u00eda Higuera, A., and Botsch, M. (2021). High Precision Outdoor and Indoor Reference State Estimation for Testing Autonomous Vehicles. Sensors, 21.","DOI":"10.3390\/s21041131"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Botsch, M., and Utschick, W. (2020). Fahrzeugsicherheit und Automatisiertes Fahren, Carl Hanser Verlag Gmbh and CO. KG.","DOI":"10.3139\/9783446468047"},{"key":"ref_13","first-page":"1","article-title":"Machine Learning Architectures for the Estimation of Predicted Occupancy Grids","volume":"9","author":"Nadarajan","year":"2018","journal-title":"J. Adv. Inf. Technol."},{"key":"ref_14","unstructured":"(2022, September 01). AI\u2014Bringing Telematics and Collision Avoidance Together. Available online: https:\/\/www.mobileye.com\/au\/fleets\/blog\/ai-bringing-telematics-and-collision-avoidance-together\/."},{"key":"ref_15","unstructured":"Jefatura de Gobierno (2022, July 02). REGLAMENTO DE TR\u00c1NSITO DE LA CIUDAD DE M\u00c9XICO. Available online: https:\/\/www.ssc.cdmx.gob.mx\/storage\/app\/media\/Transito\/Actualizaciones\/Reglamento-de-Transito-CDMX.pdf."},{"key":"ref_16","first-page":"15","article-title":"Machine Learning Based Prediction of Crash Severity Distributions for Mitigation","volume":"9","author":"Botsch","year":"2018","journal-title":"J. Adv. Inf. Technol."},{"key":"ref_17","first-page":"5998","article-title":"Attention Is All You Need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhang, J., Fang, L., Jiang, Q., and Zhou, B. (2021, January 19\u201325). Multimodal Motion Prediction with Stacked Transformers. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Virtual Conference.","DOI":"10.1109\/CVPR46437.2021.00749"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Quintanar, A., Fernandez-Llorca, D., Parra, I., Izquierdo, R., and Sotelo, M.A. (2021, January 11\u201317). Predicting Vehicles Trajectories in Urban Scenarios with Transformer Networks and Augmented Information. Proceedings of the 2021 IEEE Intelligent Vehicles Symposium (IV), Nagoya, Japan.","DOI":"10.1109\/IV48863.2021.9575242"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Mahdi, A.E., Azouz, A., Abdalla, A.E., and Abosekeen, A. (2022). A Machine Learning Approach for an Improved Inertial Navigation System Solution. Sensors, 22.","DOI":"10.3390\/s22041687"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Tan, Y., Takagi, H., and Shi, Y. (2017). Adaptive Neuro-Fuzzy Inference System: Overview, Strengths, Limitations, and Solutions. Data Mining and Big Data, Springer International Publishing.","DOI":"10.1007\/978-3-319-61845-6"},{"key":"ref_22","unstructured":"dynamics, I.S.V., and Chassis Components (2022, January 25). ISO 8855:2011. Technical Report, International Organization for Standardization. Available online: https:\/\/www.iso.org\/standard\/51180.html."},{"key":"ref_23","unstructured":"(2022, December 08). Real-Time Kinematic (RTK). Available online: https:\/\/novatel.com\/an-introduction-to-gnss\/chapter-5-resolving-errors\/real-time-kinematic-rtk."},{"key":"ref_24","unstructured":"(2022, August 31). GeneSys: ADMA-G-PRO+. Available online: https:\/\/genesys-offenburg.de\/en\/adma-g\/."},{"key":"ref_25","unstructured":"(2022, December 08). GeneSys-eigenes Kalibrier-Labor: Hohe Messqualit\u00e4t und R\u00fcckverfolgbarkeit nach ISO 17025. Available online: https:\/\/genesys-offenburg.de\/genesys-eigenes-kalibrier-labor-hohe-messqualitaet-und-rueckverfolgbarkeit-nach-iso-17025\/."},{"key":"ref_26","first-page":"10","article-title":"On the Dynamics of Automobile Drifting","volume":"1","author":"Abdulrahim","year":"2006","journal-title":"SAE Mobilus"},{"key":"ref_27","unstructured":"(2022, December 08). Accreditation Certificate DAkkS KRE. Available online: https:\/\/kistler.cdn.celum.cloud\/SAPCommerce_Download_original\/961-493d.pdf."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1002\/latj.200790155","article-title":"Optische Gitter: Die Abbildung der Realit\u00e4t \u2013 75 Jahre ber\u00fchrungslose dynamische Me\u00dftechnik auf der Basis optischer Gitter","volume":"4","author":"Haus","year":"2007","journal-title":"Laser Tech. J."},{"key":"ref_29","unstructured":"(2022, December 18). Non-Contact Optical Sensor Correvit S-Motion DTI, Standard\/2055A. Available online: https:\/\/www.kistler.com\/ES\/en\/p\/non-contact-optical-sensor-correvit-s-motion-dti-standard-2055A\/000000000018034450."},{"key":"ref_30","unstructured":"(2022, August 31). E\/E Diagnostic Test Modes. Available online: https:\/\/www.sae.org\/standards\/content\/j1979_201202\/."},{"key":"ref_31","unstructured":"Balasubramanian, L. (2022, August 31). ADMA ROS Driver. Available online: https:\/\/github.com\/lab176344\/adma_ros_driver."},{"key":"ref_32","unstructured":"Hanheide, M. (2022, August 31). Convert ROS Bag to Cvs Files. Available online: https:\/\/gist.github.com\/marc-hanheide\/4c35796e6a7cd0042dca274bf9e5e9f5."},{"key":"ref_33","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Liu, L., Liu, X., Gao, J., Chen, W., and Han, J. (2020). Understanding the Difficulty of Training Transformers. arXiv.","DOI":"10.18653\/v1\/2020.emnlp-main.463"},{"key":"ref_35","unstructured":"Xiong, R., Yang, Y., He, D., Zheng, K., Zheng, S., Xing, C., Zhang, H., Lan, Y., Wang, L., and Liu, T. (2020). On Layer Normalization in the Transformer Architecture. arXiv."},{"key":"ref_36","unstructured":"Van Miert, K. (2022, May 12). Regulation No 39 of the Economic Commission for Europe of the United Nations (UN\/ECE)\u2014Uniform Provisions Concerning the Approval of Vehicles with Regard to the Speedometer Equipment Including Its Installation. Available online: https:\/\/eur-lex.europa.eu\/LexUriServ\/LexUriServ.do?uri=OJ:L:2010:120:0040:0048:EN:PDF."},{"key":"ref_37","unstructured":"(2022, May 10). u-blox AG. NEO-6, 2011. Rev. E\u201d. Available online: https:\/\/content.u-blox.com\/sites\/default\/files\/products\/documents\/NEO-6_DataSheet_%28GPS.G6-HW-09005%29.pdf."},{"key":"ref_38","unstructured":"Research, T. (2022, December 09). Why your AI Infrastructure Needs Both Training and Inference. Available online: https:\/\/www.ibm.com\/partnerworld\/pdfs\/why-your-ai-infrastructure-needs-both-training-and-inference-platforms-10-17-19_94028894USEN.pdf."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/1\/159\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:49:35Z","timestamp":1760147375000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/1\/159"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,23]]},"references-count":38,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["s23010159"],"URL":"https:\/\/doi.org\/10.3390\/s23010159","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,23]]}}}