{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T08:48:01Z","timestamp":1782809281652,"version":"3.54.5"},"reference-count":0,"publisher":"ECMS","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,6,23]]},"abstract":"<jats:p>Accurate estimation of surrounding vehicle pose is a critical component of perception systems in highly automated vehicles (HAVs). This paper presents a computationally efficient mid-level sensor fusion algorithm for estimating oriented 3D bounding boxes of vehicles in the local coordinate frame using monocular 2D detections and ground-filtered LiDAR point clouds. The proposed method contributes to the field of simulation-driven development of autonomous systems by providing a geometry-based perception module that can be directly integrated into virtual testing environments and digital twins of highly automated vehicles. The proposed approach combines density-based clustering, projection-based camera\u2013LiDAR association via the Hungarian algorithm, and RANSAC-based geometric line fitting to construct minimum-area oriented bounding boxes in bird\u2019s-eye view. In contrast to deep learning\u2013based fusion approaches, the proposed method does not require model training, large-scale annotated datasets, or high-performance GPU hardware, which simplifies deployment and improves adaptability to non-standard sensor configurations. Experimental evaluation on a real-world dataset collected from an autonomous vehicle platform demonstrates superior mean IoU compared to classical LiDAR-only geometric methods and a purely vision-based approach. The method achieves real-time performance on embedded automotive hardware, confirming its suitability for resource-constrained autonomous systems.<\/jats:p>","DOI":"10.7148\/2026-0259","type":"proceedings-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T08:36:46Z","timestamp":1782808606000},"page":"259-266","source":"Crossref","is-referenced-by-count":0,"title":["Camera and lidar sensor fusion for projection of 2d detections into 3d coordinate space"],"prefix":"10.7148","author":[{"given":"Mikhail","family":"Chekanov","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oleg","family":"Shipitko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dmitry","family":"Nikolaev","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4144","published-online":{"date-parts":[[2026,6,23]]},"event":{"name":"40th ECMS International Conference on Modelling and Simulation"},"container-title":["ECMS 2026 Proceedings edited by Filippo Sanfilippo, Florenc Demrozi, Fabio Sgarbossa, Mohammad Poursina"],"original-title":[],"deposited":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T08:36:55Z","timestamp":1782808615000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.scs-europe.net\/dlib\/2026\/ecms2026acceptedpapers\/0259_dtsis_ecms2026_0049.pdf"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,23]]},"references-count":0,"URL":"https:\/\/doi.org\/10.7148\/2026-0259","relation":{},"subject":[],"published":{"date-parts":[[2026,6,23]]}}}