{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T10:31:43Z","timestamp":1785407503712,"version":"3.56.0"},"reference-count":35,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"HELLA GmbH & Co. KGaA"},{"name":"publication fund of the Technical University of Dortmund"},{"id":[{"id":"https:\/\/ror.org\/01k97gp34","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Pulse-width-modulated (PWM) automotive headlights enhance nighttime event-based camera detection, yet systematic parameter optimization for vulnerable road user detection remains unexplored. This study evaluates PWM frequency, duty cycle, light distribution, ego-vehicle speed, and ambient lighting under European New Car Assessment Programme-inspired crossing scenarios for cyclist and pedestrian detection. Results establish performance ranging from substantial improvements to severe degradation relative to continuous illumination. Cyclist detection achieves robust performance with high-frequency modulation across light distributions, while low-frequency operation with low beam produces severe degradation through background noise accumulation. Pedestrian detection requires high beam with street lighting enabled; low beam universally fails regardless of modulation parameters. Limited parameter combinations achieve simultaneous improvements for both targets. Detection performs optimally on retroreflective surfaces, while low-reflectivity clothing limits capability, requiring target-specific optimization.<\/jats:p>","DOI":"10.3390\/s26092595","type":"journal-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T08:11:14Z","timestamp":1776931874000},"page":"2595","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["From Light Pulses to Selective Enhancement: Performance Analysis of Event-Based Object Detection Under Pulsed Automotive Headlight Illumination"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7515-3115","authenticated-orcid":false,"given":"Leonard","family":"Haensel","sequence":"first","affiliation":[{"name":"Research Institute for Automotive Lighting and Mechatronics (L-LAB), Rixbecker Str. 75, 59557 Lippstadt, Germany"},{"name":"Institute of Control Theory and Systems Engineering (RST), Technical University Dortmund, Otto-Hahn-Str. 8, 44227 Dortmund, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6096-8190","authenticated-orcid":false,"given":"Torsten","family":"Bertram","sequence":"additional","affiliation":[{"name":"Institute of Control Theory and Systems Engineering (RST), Technical University Dortmund, Otto-Hahn-Str. 8, 44227 Dortmund, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1080\/08164622.2023.2174001","article-title":"Improving the Conspicuity and Safety of Pedestrians and Cyclists on Night-Time Roads","volume":"106","author":"Wood","year":"2023","journal-title":"Clin. Exp. Optom."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1136\/ip.2005.011056","article-title":"Road Traffic Casualties: Understanding the Night-Time Death Toll","volume":"12","author":"Plainis","year":"2006","journal-title":"Inj. Prev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1034","DOI":"10.1038\/s41586-024-07409-w","article-title":"Low-Latency Automotive Vision with Event Cameras","volume":"629","author":"Gehrig","year":"2024","journal-title":"Nature"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1109\/TPAMI.2020.3008413","article-title":"Event-Based Vision: A Survey","volume":"44","author":"Gallego","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zubi\u0107, N., Gehrig, D., Gehrig, M., and Scaramuzza, D. (2023). From Chaos Comes Order: Ordering Event Representations for Object Recognition and Detection. arXiv.","DOI":"10.1109\/ICCV51070.2023.01180"},{"key":"ref_6","unstructured":"Zhou, B., and Jiang, J. (2024). Deep Event-Based Object Detection in Autonomous Driving: A Survey. arXiv."},{"key":"ref_7","unstructured":"Cadenas, I. (2025, January 22\u201324). PWM Frequencies\u2014Feasibility to Avoid Their Negative Effects. Proceedings of the International Symposium on Automotive Lighting (ISAL), Darmstadt, Germany."},{"key":"ref_8","unstructured":"H\u00e4nsel, L., and Bertram, T. (2025, January 22\u201324). High-Frequency LED-headlight for Faster Pseudo Image Generation and Object Detection with Event-Based Cameras. Proceedings of the International Symposium on Automotive Lighting (ISAL), Darmstadt, Germany."},{"key":"ref_9","unstructured":"Dilmaghani, M.S., Shariff, W., Ryan, C., Lemley, J., and Corcoran, P. (2024). Autobiasing Event Cameras. arXiv."},{"key":"ref_10","unstructured":"H\u00e4nsel, L., and Bertram, T. (2026, January 9\u201312). Joint Characterization of PWM Headlight and Event Camera Bias Parameters for Nighttime Object Detection. Proceedings of the IEEE 103rd Vehicular Technology Conference (VTC2026-Spring), Nice, France. accepted."},{"key":"ref_11","unstructured":"Euro NCAP (2019). TEST PROTOCOL\u2014AEB VRU Systems, European New Car Assessment Programme (Euro NCAP). Technical Report Version 3.0.2."},{"key":"ref_12","unstructured":"(2026, January 22). Aldenhoven Testing Center. Available online: https:\/\/www.aldenhoven-testing-center.de\/en\/."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Fang, Y., Piao, Y., Xie, X., Li, M., Li, X., Ji, H., Xu, W., and Gao, T. (2023). Visualization and Object Detection Based on Event Information. Sensors, 23.","DOI":"10.3390\/s23041839"},{"key":"ref_14","first-page":"16639","article-title":"Learning to Detect Objects with a 1 Megapixel Event Camera","volume":"Volume 33","author":"Perot","year":"2020","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kugele, A., Pfeil, T., Pfeiffer, M., and Chicca, E. (2023, January 17\u201324). How Many Events Make an Object? Improving Single-frame Object Detection on the 1 Mpx Dataset. Proceedings of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Vancouver, BC, Canada.","DOI":"10.1109\/CVPRW59228.2023.00406"},{"key":"ref_16","unstructured":"Perot, E., de Tournemire, P., Nitti, D., Masci, J., and Sironi, A. (2020). Learning to Detect Objects with a 1 Megapixel Event Camera. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Jiang, J., Zhou, B., Zhou, T., and Zhong, Y. (2024, January 25\u201328). Deep Event-Based Object Detection in Autonomous Driving: A Survey. Proceedings of the 2024 10th International Conference on Big Data and Information Analytics (BigDIA), Chiang Mai, Thailand.","DOI":"10.1109\/BigDIA63733.2024.10808654"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"6298","DOI":"10.1109\/TIV.2024.3365991","article-title":"From Dense to Sparse: Low-Latency and Speed-Robust Event-Based Object Detection","volume":"9","author":"Fan","year":"2024","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_19","unstructured":"Lu, D., Kong, L., Lee, G.H., Chane, C.S., and Ooi, W.T. (2025). FlexEvent: Towards Flexible Event-Frame Object Detection at Varying Operational Frequencies. arXiv."},{"key":"ref_20","unstructured":"(2026, January 22). 4activeSystems\u2014EuroNCAP Certified Dummies for Active Safety. Available online: https:\/\/www.4activesystems.at\/."},{"key":"ref_21","unstructured":"Wimmer, T., and Ryrberg, S. (2026, March 23). Bicyclist Target Specifications European Automobile Manufacturers Association 2018. European Automobile Manufacturers Association (ACEA). Available online: https:\/\/www.acea.auto\/files\/Bicyclist_target-ACEA_specifications.pdf."},{"key":"ref_22","unstructured":"Wimmer, T., and Ryrberg, S. (2026, March 23). Articulated Pedestrian Target Specifications European Automobile Manufacturers Association 2015. Available online: https:\/\/www.acea.auto\/files\/Articulated_Pedestrian_Target_Specifications_-_Version_1.0.pdf."},{"key":"ref_23","unstructured":"UNECE World Forum for Harmonization of Vehicle Regulations (2021). UN Regulation No. 88: Uniform Provisions Concerning the Approval of Retro-Reflective Tyres for Two-Wheeled Vehicles, UNECE World Forum for Harmonization of Vehicle Regulations."},{"key":"ref_24","unstructured":"Schuch GmbH (2026). Datasheet: LED-Streetlight CAMINOINO 48 2403 CL (Item-Nr. 480010060), Schuch GmbH. Technical Report."},{"key":"ref_25","unstructured":"UNECE World Forum for Harmonization of Vehicle Regulations (2025). UN Regulation No. 112: Uniform Provisions Concerning the Approval of Motor Vehicle Headlamps Emitting an Asymmetrical Passing-Beam or a Driving-Beam and Equipped with Filament Lamps and\/or LED Modules, UNECE World Forum for Harmonization of Vehicle Regulations."},{"key":"ref_26","unstructured":"UNECE World Forum for Harmonization of Vehicle Regulations (2021). UN Regulation No. 149: Uniform Provisions Concerning the Approval of Road Illumination Devices and Associated Equipment for Power-Driven Vehicles, UNECE World Forum for Harmonization of Vehicle Regulations."},{"key":"ref_27","unstructured":"(2024, February 17). Roboflow: Computer Vision Tools for Developers and Enterprises. Available online: https:\/\/roboflow.com."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Verma, A.A., Chakravarthi, B., Vaghela, A., Wei, H., and Yang, Y. (2024, January 17\u201321). eTraM: Event-Based Traffic Monitoring Dataset. Proceedings of the 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.02136"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4947","DOI":"10.1109\/LRA.2021.3068942","article-title":"DSEC: A Stereo Event Camera Dataset for Driving Scenarios","volume":"6","author":"Gehrig","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yao, S., Guan, R., Huang, X., Li, Z., Sha, X., Yue, Y., Lim, E.G., Seo, H., Man, K.L., and Zhu, X. (2023). Radar-Camera Fusion for Object Detection and Semantic Segmentation in Autonomous Driving: A Comprehensive Review. arXiv.","DOI":"10.1109\/TIV.2023.3307157"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Padilla, R., Netto, S.L., and da Silva, E.A.B. (2020, January 1\u20133). A Survey on Performance Metrics for Object-Detection Algorithms. Proceedings of the 2020 International Conference on Systems, Signals and Image Processing (IWSSIP), Rio de Janeiro, Brazil.","DOI":"10.1109\/IWSSIP48289.2020.9145130"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Finateu, T., Niwa, A., Matolin, D., Tsuchimoto, K., Mascheroni, A., Reynaud, E., Mostafalu, P., Brady, F., Chotard, L., and LeGoff, F. (2020, January 16\u201320). 5.10 A 1280 \u00d7 720 Back-Illuminated Stacked Temporal Contrast Event-Based Vision Sensor with 4.86 \u00b5m Pixels, 1.066GEPS Readout, Programmable Event-Rate Controller and Compressive Data-Formatting Pipeline. Proceedings of the 2020 IEEE International Solid-State Circuits Conference\u2014(ISSCC), San Francisco, CA, USA.","DOI":"10.1109\/ISSCC19947.2020.9063149"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"102044","DOI":"10.1016\/j.jth.2025.102044","article-title":"Comparing Pedestrian and Cyclist Injuries from Falls and Collisions in British Columbia, Canada: Frequencies and Population Characteristics","volume":"42","author":"Godfreyson","year":"2025","journal-title":"J. Transp. Health"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Cui, L., Liu, H., Liu, M., Lin, E., Jiang, D., Wang, Y., and Zhu, C. (2025). PEOD: A Pixel-Aligned Event-RGB Benchmark for Object Detection under Challenging Conditions. arXiv.","DOI":"10.1609\/aaai.v40i22.38883"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"M\u00fcller, N., Waldner, M., and Bertram, T. (2025, January 4\u20137). Dynamic Matrix Headlight Illumination Strategy for Robust Object Detection in Automated Vehicles. Proceedings of the 2025 IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE), Singapore.","DOI":"10.1109\/RASSE64831.2025.11315361"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/9\/2595\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T08:14:51Z","timestamp":1776932091000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/9\/2595"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,22]]},"references-count":35,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["s26092595"],"URL":"https:\/\/doi.org\/10.3390\/s26092595","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,22]]}}}