{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T21:30:43Z","timestamp":1781299843825,"version":"3.54.1"},"reference-count":17,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,2,21]],"date-time":"2023-02-21T00:00:00Z","timestamp":1676937600000},"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 emergence of autonomous vehicles marks a shift in mobility. Conventional vehicles have been designed to prioritize the safety of drivers and passengers and increase fuel efficiency, while autonomous vehicles are developing as convergence technologies with a focus on more than just transportation. With the potential for autonomous vehicles to serve as an office or leisure space, the accuracy and stability of their driving technology is of utmost importance. However, commercializing autonomous vehicles has been challenging due to the limitations of current technology. This paper proposes a method to build a precision map for multi-sensor-based autonomous driving to improve the accuracy and stability of autonomous vehicle technology. The proposed method leverages dynamic high-definition maps to enhance the recognition rates and autonomous driving path recognition of objects in the vicinity of the vehicle, utilizing multiple sensors such as cameras, LIDAR, and RADAR. The goal is to improve the accuracy and stability of autonomous driving technology.<\/jats:p>","DOI":"10.3390\/s23052369","type":"journal-article","created":{"date-parts":[[2023,2,22]],"date-time":"2023-02-22T02:08:34Z","timestamp":1677031714000},"page":"2369","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Investigating the Improvement of Autonomous Vehicle Performance through the Integration of Multi-Sensor Dynamic Mapping Techniques"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0139-4665","authenticated-orcid":false,"given":"Hyoduck","family":"Seo","sequence":"first","affiliation":[{"name":"College of Electronics & Information, Kyunghee University, 1732 Deogyeong-daero, Giheung-gu, Yongin-si 17104, Gyeonggi-do, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyesan","family":"Lee","sequence":"additional","affiliation":[{"name":"College of Electronics & Information, Kyunghee University, 1732 Deogyeong-daero, Giheung-gu, Yongin-si 17104, Gyeonggi-do, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyujin","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Semyung University, 65 Semyung-ro, Jecheon-si 27136, Chungcheongbuk-do, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Laconte, J., Kasmi, A., Aufr\u00e8re, R., Vaidis, M., and Chapuis, R. (2022). A Survey of Localization Methods for Autonomous Vehicles in Highway Scenarios. Sensors, 22.","DOI":"10.3390\/s22010247"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zheng, L., Li, B., Yang, B., Song, H., and Lu, Z. (2019). Lane-Level Road Network Generation Techniques for Lane-Level Maps of Autonomous Vehicles: A Survey. Sustainability, 11.","DOI":"10.3390\/su11164511"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Jo, K., Chu, K., and Sunwoo, M. (2013, January 23\u201326). GPS-bias correction for precise localization of autonomous vehicles. Proceedings of the 2013 IEEE Intelligent Vehicles Symposium (IV), Gold Coast, QLD, Australia.","DOI":"10.1109\/IVS.2013.6629538"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ham, S., Im, J., Kim, M., and Cho, K. (2019). Construction and Verification of a High-Precision Base Map for an Autonomous Vehicle Monitoring System. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8110501"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wang, H., Xue, C., Zhou, Y., Wen, F., and Zhang, H. (2021\u20135, January 30). Visual Semantic Localization based on HD Map for Autonomous Vehicles in Urban Scenarios. Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi\u2019an, China.","DOI":"10.1109\/ICRA48506.2021.9561459"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"90","DOI":"10.5430\/ijfr.v9n2p90","article-title":"The Fourth Industrial Revolution: Opportunities and Challenges","volume":"9","author":"Min","year":"2018","journal-title":"Int. J. Financ. Res."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Shoykhetbrod, A., Hommes, A., and Pohl, N. (2014, January 13\u201317). A scanning FMCW-radar system for the detection of fast moving objects. Proceedings of the 2014 International Radar Conference, Lille, France.","DOI":"10.1109\/RADAR.2014.7060388"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Liu, J., Sun, Q., Fan, Z., and Jia, Y. (2018, January 4\u20137). TOF Lidar Development in Autonomous Vehicle. Proceedings of the 2018 IEEE 3rd Optoelectronics Global Conference (OGC), Shenzhen, China.","DOI":"10.1109\/OGC.2018.8529992"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5198","DOI":"10.1109\/TIP.2021.3078124","article-title":"Multi-Target Multi-Camera Tracking of Vehicles Using Metadata-Aided Re-ID and Trajectory-Based Camera Link Model","volume":"30","author":"Hsu","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wang, C., Xu, A., Sui, X., Hao, Y., Shi, Z., and Chen, Z. (2022). A Seamless Navigation System and Applications for Autonomous Vehicles Using a Tightly Coupled GNSS\/UWB\/INS\/Map Integration Scheme. Remote Sens., 14.","DOI":"10.3390\/rs14010027"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Talwar, D., Guruswamy, S., Ravipati, N., and Eirinaki, M. (2020, January 3\u20136). Evaluating Validity of Synthetic Data in Perception Tasks for Autonomous Vehicles. Proceedings of the 2020 IEEE International Conference on Artificial Intelligence Testing (AITest), Oxford, UK.","DOI":"10.1109\/AITEST49225.2020.00018"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1766","DOI":"10.1109\/TCST.2020.3006123","article-title":"An LPV Approach to Autonomous Vehicle Path Tracking in the Presence of Steering Actuation Nonlinearities","volume":"29","author":"Corno","year":"2021","journal-title":"IEEE Trans. Control. Syst. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Dang, X., Rong, Z., and Liang, X. (2021). Sensor Fusion-Based Approach to Eliminating Moving Objects for SLAM in Dynamic Environments. Sensors, 21.","DOI":"10.3390\/s21010230"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Purf\u00fcrst, T. (2022). Evaluation of Static Autonomous GNSS Positioning Accuracy Using Single-, Dual-, and Tri-Frequency Smartphones in Forest Canopy Environments. Sensors, 22.","DOI":"10.3390\/s22031289"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chung, W.Y., Kim, S.Y., and Kang, C.H. (2022). Image Dehazing Using LiDAR Generated Grayscale Depth Prior. Sensors, 22.","DOI":"10.3390\/s22031199"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Seo, H., Kim, H., Lee, K., and Lee, K. (2022). Multi-Sensor-Based Blind-Spot Reduction Technology and a Data-Logging Method Using a Gesture Recognition Algorithm Based on Micro E-Mobility in an IoT Environment. Sensors, 22.","DOI":"10.3390\/s22031081"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Jim\u00e9nez, F., Clavijo, M., and Cerrato, A. (2022). Perception, Positioning and Decision-Making Algorithms Adaptation for an Autonomous Valet Parking System Based on Infrastructure Reference Points Using One Single LiDAR. Sensors, 22.","DOI":"10.3390\/s22030979"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/5\/2369\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:37:56Z","timestamp":1760121476000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/5\/2369"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,21]]},"references-count":17,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["s23052369"],"URL":"https:\/\/doi.org\/10.3390\/s23052369","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,21]]}}}