{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T23:30:57Z","timestamp":1779319857754,"version":"3.51.4"},"reference-count":48,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,15]],"date-time":"2021-11-15T00:00:00Z","timestamp":1636934400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The virtual testing and validation of advanced driver assistance system and automated driving (ADAS\/AD) functions require efficient and realistic perception sensor models. In particular, the limitations and measurement errors of real perception sensors need to be simulated realistically in order to generate useful sensor data for the ADAS\/AD function under test. In this paper, a novel sensor modeling approach for automotive perception sensors is introduced. The novel approach combines kernel density estimation with regression modeling and puts the main focus on the position measurement errors. The modeling approach is designed for any automotive perception sensor that provides position estimations at the object level. To demonstrate and evaluate the new approach, a common state-of-the-art automotive camera (Mobileye 630) was considered. Both sensor measurements (Mobileye position estimations) and ground-truth data (DGPS positions of all attending vehicles) were collected during a large measurement campaign on a Hungarian highway to support the development and experimental validation of the new approach. The quality of the model was tested and compared to reference measurements, leading to a pointwise position error of 9.60% in the lateral and 1.57% in the longitudinal direction. Additionally, the modeling of the natural scattering of the sensor model output was satisfying. In particular, the deviations of the position measurements were well modeled with this approach.<\/jats:p>","DOI":"10.3390\/s21227583","type":"journal-article","created":{"date-parts":[[2021,11,15]],"date-time":"2021-11-15T20:46:47Z","timestamp":1637009207000},"page":"7583","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Development and Experimental Validation of an Intelligent Camera Model for Automated Driving"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7537-2058","authenticated-orcid":false,"given":"Simon","family":"Genser","sequence":"first","affiliation":[{"name":"Virtual Vehicle Research GmbH, Inffeldgasse 21a, 8010 Graz, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1920-8437","authenticated-orcid":false,"given":"Stefan","family":"Muckenhuber","sequence":"additional","affiliation":[{"name":"Virtual Vehicle Research GmbH, Inffeldgasse 21a, 8010 Graz, Austria"},{"name":"Department of Geography and Regional Science, University of Graz, Heinrichstra\u00dfe 36, 8010 Graz, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0686-1306","authenticated-orcid":false,"given":"Selim","family":"Solmaz","sequence":"additional","affiliation":[{"name":"Virtual Vehicle Research GmbH, Inffeldgasse 21a, 8010 Graz, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7105-4554","authenticated-orcid":false,"given":"Jakob","family":"Reckenzaun","sequence":"additional","affiliation":[{"name":"Virtual Vehicle Research GmbH, Inffeldgasse 21a, 8010 Graz, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,15]]},"reference":[{"key":"ref_1","unstructured":"World Health Organisation (2020, January 20). Global Status Report on Road Safety 2018, Available online: https:\/\/apps.who.int\/iris\/bitstream\/handle\/10665\/276462\/9789241565684-eng.pdf."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Anderson, J.M., Kalra, N., Stanley, K.D., Sorensen, P., Samaras, C., and Oluwatola, O.A. (2016). Autonomous Vehicle Technology: A Guide for Policymakers, RAND Corporation. Available online: http:\/\/www.rand.org\/pubs\/research_reports\/RR443-2.html.","DOI":"10.7249\/RR443-2"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.tra.2015.04.003","article-title":"Preparing a nation for autonomous vehicles: Opportunities, barriers and policy recommendations","volume":"77","author":"Fagnant","year":"2015","journal-title":"Transp. Res. Part Policy Pract."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Watzenig, D., and Horn, M. (2016). Automated Driving: Safer and More Efficient Future Driving, Springer.","DOI":"10.1007\/978-3-319-31895-0"},{"key":"ref_5","unstructured":"SAE International (2021, May 31). Ground Vehicle Standard J3016_201806. Available online: https:\/\/saemobilus.sae.org\/content\/j3016_201806."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1109\/MITS.2019.2907630","article-title":"A Review of Sensor Technologies for Perception in Automated Driving","volume":"11","author":"Marti","year":"2019","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Winner, H., Hakuli, S., Lotz, F., and Singer, C. (2016). Handbook of Driver Assistance Systems, Springer International Publishing. [1st ed.].","DOI":"10.1007\/978-3-319-12352-3"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"100016","DOI":"10.1016\/j.array.2020.100016","article-title":"Distance Measurement System for Autonomous Vehicles Using Stereo Camera","volume":"5","author":"Zaarane","year":"2020","journal-title":"Array"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"4805","DOI":"10.3390\/s100504805","article-title":"Real Time Speed Estimation of Moving Vehicles from Side View Images from an Uncalibrated Video Camera","volume":"10","author":"Dogan","year":"2010","journal-title":"Sensors"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Xique, I.J., Buller, W., Fard, Z.B., Dennis, E., and Hart, B. (2018, January 1\u20135). Evaluating Complementary Strengths and Weaknesses of ADAS Sensors. Proceedings of the 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall), Chicago, IL, USA.","DOI":"10.1109\/VTCFall.2018.8690901"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.tra.2016.09.010","article-title":"Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability?","volume":"94","author":"Kalra","year":"2016","journal-title":"Transp. Res. Part Policy Pract."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Winner, H., Hakuli, S., Lotz, F., and Singer, C. (2015). Virtuelle Integration\u2019 Kapitel 8 in \u2018Handbuch Fahrerassistenzsysteme\u20142015, Grundlagen, Komponenten und Systeme Fuer Aktive Sicherheit und Komfort, Springer.","DOI":"10.1007\/978-3-658-05734-3"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Solmaz, S., and Holzinger, F. (2019, January 4\u20138). A Novel Testbench for Development, Calibration and Functional Testing of ADAS\/AD Functions. Proceedings of the 2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE), Graz, Austria.","DOI":"10.1109\/ICCVE45908.2019.8965225"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Solmaz, S., Rudigier, M., and Mischinger, M. (November, January 19). A Vehicle-in-the-Loop Methodology for Evaluating Automated Driving Functions in Virtual Traffic. Proceedings of the 2020 IEEE Intelligent Vehicles Symposium (IV), Las Vegas, NV, USA.","DOI":"10.1109\/IV47402.2020.9304811"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"133","DOI":"10.4271\/12-04-01-0011","article-title":"Hybrid Testing: A Vehicle-in-the-Loop Testing Method for the Development of Automated Driving Functions","volume":"4","author":"Solmaz","year":"2021","journal-title":"SAE Intl. J. CAV"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Solmaz, S., Holzinger, F., Mischinger, M., Rudigier, M., and Reckenzaun, J. (2021). Novel Hybrid-Testing Paradigms for Automated Vehicle and ADAS Function Development. Towards Connected and Autonomous Vehicle Highway: Technical, Security and Ethical Challenges, Springer. EAI\/Springer Innovations in Communications and Computing Book Series.","DOI":"10.1007\/978-3-030-66042-0_8"},{"key":"ref_17","unstructured":"VIRES Simulationstechnologie GmbH (2021, May 31). VTD\u2014VIRES Virtual Test Drive. Available online: https:\/\/vires.mscsoftware.com."},{"key":"ref_18","unstructured":"IPG Automotive GmbH (2021, May 31). CarMaker: Virtual Testing of Automobiles and Light-Duty Vehicles. Available online: https:\/\/ipg-automotive.com\/products-services\/simulation-software\/carmaker\/."},{"key":"ref_19","unstructured":"Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., and Koltun, V. (2017, January 13\u201315). CARLA: An Open Urban Driving Simulator. Proceedings of the 1st Annual Conference on Robot Learning, Mountain View, CA, USA."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hutter, M., and Siegwart, R. (2018). AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles. Field and Service Robotics, Springer. Springer Proceedings in Advanced Robotics.","DOI":"10.1007\/978-3-319-67361-5"},{"key":"ref_21","unstructured":"AIMotive (2021, May 31). aiSim\u2014The World\u2019s First ISO26262 ASIL-D Certified Simulator Tool. Available online: https:\/\/aimotive.com\/aisim."},{"key":"ref_22","unstructured":"Hanke, T., Hirsenkorn, N., van-Driesten, C., Garcia-Ramos, P., Schiementz, M., Schneider, S., and Biebl, E. (2021, November 12). Open Simulation Interface\u2014A Generic Interface for the Environment Perception of Automated Driving Functions in Virtual Scenarios. Research Report. Available online: https:\/\/www.hot.ei.tum.de\/forschung\/automotive-veroeffentlichungen\/."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"233","DOI":"10.4271\/12-03-03-0018","article-title":"State-of- the-Art Sensor Models for Virtual Testing of Advanced Driver Assistance Systems\/Autonomous Driving Functions","volume":"3","author":"Schlager","year":"2020","journal-title":"SAE Int. J. CAV"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hanke, T., Hirsenkorn, N., Dehlink, B., Rauch, A., Rasshofer, R., and Biebl, E. (2015, January 24\u201326). Generic Architecture for Simulation of ADAS Sensors. Proceedings of the 2015 Proceedings International Radar Symposium, Dresden, Germany.","DOI":"10.1109\/IRS.2015.7226306"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Muckenhuber, S., Holzer, H., R\u00fcbsam, J., and Stettinger, G. (2019, January 4\u20138). Object-based sensor model for virtual testing of ADAS\/AD functions. Proceedings of the 2019 IEEE International Conference on Connected Vehicles and Expo (ICCVE), Graz, Austria.","DOI":"10.1109\/ICCVE45908.2019.8965071"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Schmidt, S., Schlager, B., Muckenhuber, S., and Stark, R. (2021). Configurable Sensor Model Architecture for the Development of Automated Driving Systems. Sensors, 21.","DOI":"10.3390\/s21144687"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1007\/s00502-018-0629-0","article-title":"Fast Generic Sensor Models for Testing Highly Automated Vehicles in Simulation","volume":"135","author":"Stolz","year":"2018","journal-title":"Elektrotechnik Informationstechnik"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Hirsenkorn, N., Hanke, T., Rauch, A., Dehlink, B., Rasshofer, R., and Biebl, E. (2015, January 24\u201326). A non-parametric approach for modeling sensor behavior. Proceedings of the 16th International Radar Symposium, Dresden, Germany.","DOI":"10.1109\/IRS.2015.7226346"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Carlson, A., Skinner, K.A., Vasudevan, R., and Roberson, M.J. (2018, January 8\u201314). Modeling Camera Effects to Improve Visual Learning from Synthetic Data. Proceedings of the Computer Vision\u2014ECCV 2018 Workshops, Munich, Germany.","DOI":"10.1007\/978-3-030-11009-3_31"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2431","DOI":"10.1109\/LRA.2019.2896470","article-title":"Sensor Transfer: Learning Optimal Sensor Effect Image Augmentation for Sim-to-Real Domain Adaptation","volume":"4","author":"Carlson","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1007\/s00502-018-0622-7","article-title":"Camera Behavioral Model and Testbed Setups for Image-Based ADAS Functions","volume":"135","author":"Schneider","year":"2018","journal-title":"Elektrotechnik Informationstechnik"},{"key":"ref_32","first-page":"1","article-title":"Realistic Image Degradation with Measured PSF","volume":"2018","author":"Wittpahl","year":"2018","journal-title":"Electron. Imaging Auton. Veh. Mach."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1109\/TIV.2018.2886678","article-title":"Test Your Self-Driving Algorithm: An Overview of Publicly Available Driving Datasets and Virtual Testing Environments","volume":"4","author":"Kang","year":"2019","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_34","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B. (July, January 26). The Cityscapes Dataset for Semantic Urban Scene Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Huang, X., Cheng, X., Geng, Q., Cao, B., Zhou, D., Wang, P., Lin, Y., and Yang, R. (2018, January 18\u201322). The ApolloScape Dataset for Autonomous Driving. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00141"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Huang, X., Wang, P., Cheng, X., Zhou, D., Geng, Q., and Yang, R. (2019). The ApolloScape Open Dataset for Autonomous Driving and its Application. arXiv.","DOI":"10.1109\/CVPRW.2018.00141"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Xu, H., Gao, Y., Yu, F., and Darrell, T. (2017, January 21\u201326). End-to-end learning of driving models from large-scale video datasets. Proceedings of the IEEE Conference on Computer Vision Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.376"},{"key":"ref_38","unstructured":"Yu, F., Xian, W., Chen, Y., Liu, F., Liao, M., Madhavan, V., and Darrell, T. (2018). BDD100K: A diverse driving video database with scalable annotation tooling. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., and Urtasun, R. (2012, January 16\u201321). Are we ready for autonomous driving? The KITTI vision benchmark suite. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1177\/0278364913491297","article-title":"Vision Meets Robotics: The KITTI Dataset","volume":"32","author":"Geiger","year":"2013","journal-title":"Int. J. Robot. Res."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., and Caine, B. (2019). Scalability in Perception for Autonomous Driving: Waymo Open Dataset. arXiv.","DOI":"10.1109\/CVPR42600.2020.00252"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., and Beijbom, O. (2019). nuScenes: A multimodal dataset for autonomous driving. arXiv.","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2169","DOI":"10.3390\/s21062169","article-title":"Motorway Measurement Campaign to Support R&D Activities in the Field of Automated Driving Technologies","volume":"21","author":"Tihanyi","year":"2021","journal-title":"Sensors"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Parzen, E. (1962). On Estimation of a Probability Density Function and Mode, Stanford University.","DOI":"10.1214\/aoms\/1177704472"},{"key":"ref_45","unstructured":"Turlach, B.A. (1999). Bandwidth Selection in Kernel Density Estimation: A Review, Universite Catholique de Louvain."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Muckenhuber, S., Museljic, E., and Stettinger, G. (2021). Performance evaluation of a state-of-the-art automotive radar and corresponding modeling approaches based on a large labeled dataset. J. Intell. Transp. Syst., 1\u201320.","DOI":"10.1080\/15472450.2021.1959328"},{"key":"ref_47","unstructured":"Austrian Ministry for Transport, Innovation and Technology (2021, August 17). Austrian Action Programme on Automated Mobility 2019\u20132022. Vienna 2018, Available online: https:\/\/www.bmk.gv.at."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Solmaz, S., Muminovic, R., Civgin, A., and Stettinger, G. (2021, January 19\u201322). Development, Analysis and Real-life Benchmarking of RRT-based Path Planning Algorithms for Automated Valet Parking. Proceedings of the 24th IEEE International Intelligent Transportation Systems Conference (ITSC21), Indianapolis, IN, USA.","DOI":"10.1109\/ITSC48978.2021.9564413"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/22\/7583\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:30:31Z","timestamp":1760167831000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/22\/7583"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,15]]},"references-count":48,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["s21227583"],"URL":"https:\/\/doi.org\/10.3390\/s21227583","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,15]]}}}