{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T07:15:05Z","timestamp":1761808505845,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,4,14]],"date-time":"2022-04-14T00:00:00Z","timestamp":1649894400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"BK21 FOUR project funded by the Ministry of Education, Korea","award":["4199990113966"],"award-info":[{"award-number":["4199990113966"]}]},{"name":"Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT","award":["NRF-2019R1A2C2005099, NRF-2018R1A6A1A03025109"],"award-info":[{"award-number":["NRF-2019R1A2C2005099, NRF-2018R1A6A1A03025109"]}]},{"name":"Institute of Information and communications Technology Planning and Evaluation (IITP) grant funded by the Korea government (MSIT)","award":["No. 2021-0-00944, Metamorphic approach of unstructured validation\/verification for analyzing binary code"],"award-info":[{"award-number":["No. 2021-0-00944, Metamorphic approach of unstructured validation\/verification for analyzing binary code"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Forward vehicle detection is the key technique to preventing car incidents in front. Artificial intelligence (AI) techniques are used to more accurately detect vehicles, but AI-based vehicle detection takes a lot of processing time due to its high computational complexity. When there is a risk of collision with a vehicle in front, the slow detection speed of the vehicle may lead to an accident. To quickly detect a vehicle in real-time, a high-speed and lightweight vehicle detection technique with similar detection performance to that of an existing AI-based vehicle detection is required. In addition, to apply forward collision warning system (FCWS) technology to vehicles, it is important to provide high performance based on low-power embedded systems because the vehicle\u2019s battery consumption must remain low. The vehicle detection algorithm occupies the most resources in FCWS. To reduce power consumption, it is important to reduce the computational complexity of an algorithm, that is, the amount of resources required to run it. This paper describes a method for fast, accurate forward vehicle detection using machine learning and deep learning. To detect a vehicle in consecutive images consistently, a Kalman filter is used to predict the bounding box based on the tracking algorithm and correct it based on the detection algorithm. As a result, its vehicle detection speed is about 25.85 times faster than deep-learning-based object detection is, and its detection accuracy is better than machine-learning-based object detection is.<\/jats:p>","DOI":"10.3390\/s22082998","type":"journal-article","created":{"date-parts":[[2022,4,19]],"date-time":"2022-04-19T02:39:31Z","timestamp":1650335971000},"page":"2998","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Runtime ML-DL Hybrid Inference Platform Based on Multiplexing Adaptive Space-Time Resolution for Fast Car Incident Prevention in Low-Power Embedded Systems"],"prefix":"10.3390","volume":"22","author":[{"given":"Sunghoon","family":"Hong","sequence":"first","affiliation":[{"name":"School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5560-873X","authenticated-orcid":false,"given":"Daejin","family":"Park","sequence":"additional","affiliation":[{"name":"School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,14]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2021, June 21). 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