{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T18:04:13Z","timestamp":1780337053975,"version":"3.54.1"},"reference-count":42,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T00:00:00Z","timestamp":1779408000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"GTB"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Perception-oriented evaluation of automotive headlamps still relies mainly on human-vision photometric criteria, although forward-facing cameras are increasingly safety-critical sensing elements for night driving. This paper benchmarks 16 measured production headlamp light distributions with a simulation chain that combines headlamp spectra and beam patterns, diffuse scene reflection, an imaging-transfer model, and an EMVA-based camera model. The quantitative chain maps scene radiance to sensor-domain signal-to-noise ratio, derives task-specific required signal-to-noise curves from a six-network object-recognition ensemble, and aggregates local threshold satisfaction as region-of-interest coverage across three target reflectances and five driving speeds using WLTP moving-time weights. For the baseline RGB camera, WLTP-weighted coverage ranges from 18.95% to 53.48% across the evaluated light distributions, corresponding to a factor of 2.82 between the weakest and strongest distribution. The camera-parameter sweeps show that favorable beam placement can deliver comparable benchmark coverage with roughly 60% smaller pixel pitch than the weakest distribution, corresponding to an 84% reduction in pixel area, or at materially shorter exposure times. The WLTP-weighted coverage score correlates positively with the established Headlamp Safety Performance Rating, with Pearson r=0.68 for the RGB configuration, indicating partial alignment between human-centric and camera-centric illumination needs while confirming that the metrics are not interchangeable. The results identify headlamp light distribution as a primary design parameter for nighttime camera perception and provide a quantitative basis for co-design of automotive lighting and camera-based systems.<\/jats:p>","DOI":"10.3390\/s26113290","type":"journal-article","created":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T12:14:52Z","timestamp":1779452092000},"page":"3290","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Quantifying the Impact of Headlamp Light Distribution on Automotive Camera Perception: Establishing a New Primary Design Parameter"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5288-159X","authenticated-orcid":false,"given":"David","family":"Hoffmann","sequence":"first","affiliation":[{"name":"Laboratory of Adaptive Lighting Systems and Visual Processing, Technische Universit\u00e4t Darmstadt, Hochschulstr. 4a, 64289 Darmstadt, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1219-3548","authenticated-orcid":false,"given":"Julian","family":"Lerch","sequence":"additional","affiliation":[{"name":"Laboratory of Adaptive Lighting Systems and Visual Processing, Technische Universit\u00e4t Darmstadt, Hochschulstr. 4a, 64289 Darmstadt, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9627-226X","authenticated-orcid":false,"given":"Korbinian","family":"Kunst","sequence":"additional","affiliation":[{"name":"Laboratory of Adaptive Lighting Systems and Visual Processing, Technische Universit\u00e4t Darmstadt, Hochschulstr. 4a, 64289 Darmstadt, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8042-1101","authenticated-orcid":false,"given":"Nikolai","family":"Kre\u00df","sequence":"additional","affiliation":[{"name":"Laboratory of Adaptive Lighting Systems and Visual Processing, Technische Universit\u00e4t Darmstadt, Hochschulstr. 4a, 64289 Darmstadt, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1828-2459","authenticated-orcid":false,"given":"Tran Quoc","family":"Khanh","sequence":"additional","affiliation":[{"name":"Laboratory of Adaptive Lighting Systems and Visual Processing, Technische Universit\u00e4t Darmstadt, Hochschulstr. 4a, 64289 Darmstadt, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,22]]},"reference":[{"key":"ref_1","unstructured":"Mordor Intelligence (2025, October 25). Automotive Camera Market. Available online: https:\/\/www.mordorintelligence.com\/industry-reports\/automotive-camera-market."},{"key":"ref_2","unstructured":"Grand View Research (2025, October 25). Autonomous Vehicles Market Size, Share and Trends Analysis Report, 2018\u20132030, 2024. Available online: https:\/\/www.grandviewresearch.com\/industry-analysis\/autonomous-vehicles-market."},{"key":"ref_3","unstructured":"GTB Working Group Safety and Visual Performance (WG-SVP) (2023). Headlight Safety Performance Rating (HSPR) Recommended Practice, GTB Automotive Lighting and Light Signalling Expert Group."},{"key":"ref_4","unstructured":"Commission Internationale de l\u2019\u00c9clairage (CIE) (2010). Performance Assessment Method for Vehicle Headlighting Systems, CIE Central Bureau. Number CIE 188:2010 in CIE Publication."},{"key":"ref_5","unstructured":"Hoffmann, D. (2026). Data- and Simulation-Based Characterization of the Photonic Relationship Between Forward-Facing Cameras and Vehicle Front Lighting Functions for Adaptive Driver Assistance Systems. [Ph.D. Thesis, Technische Universit\u00e4t Darmstadt]."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wang, L., Ma, J., Su, P., and Huang, J. (2021). High-Resolution Pixel LED Headlamps: Functional Requirement Analysis and Research Progress. Appl. Sci., 11.","DOI":"10.3390\/app11083368"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Held, M.P., Kloppenburg, G., and Lachmayer, R. (2019, January 2\u20137). Micro pixel LEDs: Design challenge and implementation for high-resolution headlamps. Proceedings of the Light-Emitting Devices, Materials, and Applications, San Francisco, CA, USA.","DOI":"10.1117\/12.2509336"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"26466","DOI":"10.1364\/OE.429951","article-title":"Laser-assisted LED for adaptive-driving-beam headlights employing ultra-reliable single crystal phosphor for autonomous vehicles","volume":"29","author":"Chang","year":"2021","journal-title":"Opt. Express"},{"key":"ref_9","unstructured":"Kobbert, J. (2019). Optimization of Automotive Light Distributions for Different Real Life Traffic Situations. [Ph.D. Thesis, Technische Universit\u00e4t Darmstadt]."},{"key":"ref_10","unstructured":"Erkan, A. (2023). Optimierung von Frontscheinwerferlichtverteilungen Anhand Wahrnehmungsphysiologischer Kriterien. [Ph.D. Thesis, Technische Universit\u00e4t Darmstadt]."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"8","DOI":"10.53375\/ijecer.2022.295","article-title":"Energy-Efficient Illumination by Matrix Headlamps for Nighttime Automated Object Detection","volume":"2","author":"Waldner","year":"2022","journal-title":"Int. J. Electr. Comput. Eng. Res."},{"key":"ref_12","unstructured":"Waldner, M., M\u00fcller, N., and Bertram, T. (2023, January 23\u201325). Different Approaches to Optimize High-Definition Matrix Headlights to Improve Computer Vision. Proceedings of the Lux Junior 2023-16th International Forum for Young Lighting Engineers, Ilmenau, Germany."},{"key":"ref_13","unstructured":"M\u00fcller, N. (2024, January 6\u20138). Concept of a Matrix Headlight Control Loop for Enhanced Object Detection in Automated Driving. Proceedings of the Lux Junior 2024-17th International Forum for Young Lighting Engineers, Ilmenau, Germany."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.neucom.2017.05.015","article-title":"Automatic illumination planning for robot vision inspection system","volume":"275","author":"Wang","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Howard, A., Sandler, M., Chen, B., Wang, W., Chen, L.C., Tan, M., Chu, G., Vasudevan, V., Zhu, Y., and Pang, R. (November, January 27). Searching for MobileNetV3. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00140"},{"key":"ref_17","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2021). An Image is Worth 16\u00d716 Words: Transformers for Image recognition at Scale. arXiv."},{"key":"ref_18","unstructured":"Tan, M., and Le, Q.V. (2021). EfficientNetV2: Smaller Models and Faster Training. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liu, Z., Hu, H., Lin, Y., Yao, Z., Xie, Z., Wei, Y., Ning, J., Cao, Y., Zhang, Z., and Dong, L. (2022). Swin Transformer V2: Scaling Up Capacity and Resolution. arXiv.","DOI":"10.1109\/CVPR52688.2022.01170"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Woo, S., Debnath, S., Hu, R., Chen, X., Liu, Z., Kweon, I.S., and Xie, S. (2023, January 17\u201324). ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders. Proceedings of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01548"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"art00003","DOI":"10.2352\/ISSN.2470-1173.2019.15.AVM-030","article-title":"Contrast detection probability-Implementation and use cases","volume":"31","author":"Artmann","year":"2019","journal-title":"Electron. Imaging"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez-Rodr\u00edguez, J.A., L\u00f3pez-Rubio, E., \u00c1ngel Ruiz, J.A., and Molina-Cabello, M.A. (2024). The Impact of Noise and Brightness on Object Detection Methods. Sensors, 24.","DOI":"10.3390\/s24030821"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"22210","DOI":"10.1109\/JSEN.2022.3211406","article-title":"Analysis of Automotive Camera Sensor Noise Factors and Impact on Object Detection","volume":"22","author":"Li","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"0219","DOI":"10.34133\/remotesensing.0219","article-title":"A Comprehensive Study on the Robustness of Deep Learning-Based Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarking","volume":"4","author":"Mei","year":"2024","journal-title":"J. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1145\/882262.882343","article-title":"A Data-Driven Reflectance Model","volume":"22","author":"Matusik","year":"2003","journal-title":"ACM Trans. Graph."},{"key":"ref_26","unstructured":"Mitsubishi Electric Research Laboratories (2026, May 13). MERL BRDF Database. Available online: https:\/\/www.merl.com\/brdf\/."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Holst, G.C., and Lomheim, T.S. (2011). CMOS\/CCD Sensors and Camera Systems, SPIE Press. [2nd ed.].","DOI":"10.1117\/3.2524677"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2352\/EI.2023.35.6.ISS-347","article-title":"Implementation of EMVA 1288 standard release 4.0 for characterization of image sensors","volume":"35","author":"Borek","year":"2023","journal-title":"Electron. Imaging"},{"key":"ref_29","unstructured":"European Machine Vision Association (2021). Standard for Characterization of Image Sensors and Cameras, Release 4.0 Linear, EMVA. Technical Report."},{"key":"ref_30","unstructured":"Johnson, J. (2024). Understanding Noise and Noise Reduction in CMOS Imaging Sensors: Including Notes on Temporal and Spatial Noise Reduction, Steward Observatory, University of Arizona, Space Astrophysics Laboratory. Technical Report."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"435","DOI":"10.4271\/2011-01-0576","article-title":"Method for Estimating Time to Collision at Braking in Real-World, Lead Vehicle Stopped Rear-End Crashes for Use in Pre-Crash System Design","volume":"4","author":"Kusano","year":"2011","journal-title":"SAE Int. J. Passeng. Cars-Mech. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"107","DOI":"10.4271\/2016-01-1439","article-title":"Forward Collision Warning: Clues to Optimal Timing of Advisory Warnings","volume":"4","author":"Aksan","year":"2016","journal-title":"SAE Int. J. Transp. Saf."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"e27665","DOI":"10.1016\/j.heliyon.2024.e27665","article-title":"Conflict-Based safety evaluations at unsignalized intersections using surrogate safety measures","volume":"10","author":"Singh","year":"2024","journal-title":"Heliyon"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Deveaux, D., Higuchi, T., Ucar, S., Wang, C.H., Harri, J., and Altintas, O. (2021, January 19\u201322). Extraction of Risk Knowledge from Time to Collision Variation in Roundabouts. Proceedings of the 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), Indianapolis, IN, USA.","DOI":"10.1109\/ITSC48978.2021.9564570"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Mu\u00f1oz S\u00e1nchez, M., van der Ploeg, C., Smit, R.M., Elfring, J., Silvas, E., and van de Molengraft, R.M. (2024). Prediction Horizon Requirements for Automated Driving: Optimizing Safety, Comfort, and Efficiency. arXiv.","DOI":"10.1109\/IV55156.2024.10588728"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1207\/STHF0203_1","article-title":"How Long Does It Take to Stop? Methodological Analysis of Driver Perception\u2013Brake Times","volume":"2","author":"Green","year":"2000","journal-title":"Transp. Hum. Factors"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Betz, T., Schmeller, M., Korb, A., and Betz, J. (2023). Latency Measurement for Autonomous Driving Software Using Data Flow Extraction. Proceedings of the 2023 IEEE Intelligent Vehicles Symposium (IV), IEEE.","DOI":"10.1109\/IV55152.2023.10186686"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"283","DOI":"10.4271\/12-06-03-0018","article-title":"An Analysis of Software Latency for a High-Speed Autonomous Race Car\u2014A Case Study in the Indy Autonomous Challenge","volume":"6","author":"Betz","year":"2023","journal-title":"SAE Int. J. Connect. Autom. Veh."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Madjid, N.A., Ahmad, A., Mebrahtu, M., Babaa, Y., Nasser, A., Malik, S., Hassan, B., Werghi, N., Dias, J., and Khonji, M. (2025). Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions. arXiv.","DOI":"10.1016\/j.inffus.2025.103588"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B. (2016). The Cityscapes Dataset for Semantic Urban Scene Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref_41","unstructured":"European Commission (2017). Commission Regulation (EU) 2017\/1151 of 1 June 2017 Supplementing Regulation (EC) No 715\/2007 of the European Parliament and of the Council on Type-Approval of Motor Vehicles with Respect to Emissions from Light Passenger and Commercial Vehicles (Euro 5 and Euro 6) and on Access to Vehicle Repair and Maintenance Information, Amending Directive 2007\/46\/EC of the European Parliament and of the Council, Commission Regulation (EC) No 692\/2008 and Commission Regulation (EU) No 1230\/2012 and Repealing Commission Regulation (EC) No 692\/2008. Off. J. Eur. Union, L 175, 1\u2013643. Available online: https:\/\/eur-lex.europa.eu\/legal-content\/EN\/TXT\/?uri=CELEX:32017R1151."},{"key":"ref_42","unstructured":"European Commission, Joint Research Centre (2026, May 13). V_class3b.txt: WLTC Class 3b Velocity Trace. JRCSTU\/wltp GitHub Repository. Available online: https:\/\/raw.githubusercontent.com\/JRCSTU\/wltp\/master\/wltp\/cycles\/V_class3b.txt."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/11\/3290\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T17:11:32Z","timestamp":1780333892000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/11\/3290"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,22]]},"references-count":42,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["s26113290"],"URL":"https:\/\/doi.org\/10.3390\/s26113290","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,22]]}}}