{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:26:13Z","timestamp":1783437973328,"version":"3.54.6"},"reference-count":23,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T00:00:00Z","timestamp":1668556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and Technology (MOST)","award":["MOST 110-2221-E-027-087"],"award-info":[{"award-number":["MOST 110-2221-E-027-087"]}]},{"name":"Ministry of Science and Technology (MOST)","award":["NSTC 111-2218-E-035-001"],"award-info":[{"award-number":["NSTC 111-2218-E-035-001"]}]},{"name":"National Science and Technology Council (NSTC)","award":["MOST 110-2221-E-027-087"],"award-info":[{"award-number":["MOST 110-2221-E-027-087"]}]},{"name":"National Science and Technology Council (NSTC)","award":["NSTC 111-2218-E-035-001"],"award-info":[{"award-number":["NSTC 111-2218-E-035-001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate distance estimation is a requirement for advanced driver assistance systems (ADAS) to provide drivers with safety-related functions such as adaptive cruise control and collision avoidance. Radars and lidars can be used for providing distance information; however, they are either expensive or provide poor object information compared to image sensors. In this study, we propose a lightweight convolutional deep learning model that can extract object-specific distance information from monocular images. We explore a variety of training and five structural settings of the model and conduct various tests on the KITTI dataset for evaluating seven different road agents, namely, person, bicycle, car, motorcycle, bus, train, and truck. Additionally, in all experiments, a comparison with the Monodepth2 model is carried out. Experimental results show that the proposed model outperforms Monodepth2 by 15% in terms of the average weighted mean absolute error (MAE).<\/jats:p>","DOI":"10.3390\/s22228846","type":"journal-article","created":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T04:39:03Z","timestamp":1668573543000},"page":"8846","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Supervised Object-Specific Distance Estimation from Monocular Images for Autonomous Driving"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8596-4196","authenticated-orcid":false,"given":"Yury","family":"Davydov","sequence":"first","affiliation":[{"name":"Graduate Institute of Automation Technology, National Taipei University of Technology, Taipei 10608, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2015-5256","authenticated-orcid":false,"given":"Wen-Hui","family":"Chen","sequence":"additional","affiliation":[{"name":"Graduate Institute of Automation Technology, National Taipei University of Technology, Taipei 10608, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6735-0473","authenticated-orcid":false,"given":"Yu-Chen","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Automatic Control Engineering, Feng Chia University, Taichung 40724, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Godard, C., Mac Aodha, O., and Brostow, G. 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