{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T03:40:20Z","timestamp":1782877220560,"version":"3.54.5"},"reference-count":40,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,16]],"date-time":"2022-03-16T00:00:00Z","timestamp":1647388800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Sensors"],"abstract":"<jats:p>Understanding the operation of complex assets such heavy-duty vehicles is essential for improving the efficiency, sustainability, and safety of future industry. Specifically, reducing energy consumption of transportation is crucially important for fleet operators, due to the impact it has on decreasing energy costs and lowering greenhouse gas emissions. Drivers have a high influence on fuel usage. However, reliably estimating driver performance is challenging. This is a key component of many eco-driving tools used to train drivers. Some key aspects of good, or efficient, drivers include being more aware of the surroundings, adapting to the road situations, and anticipating likely developments of the traffic conditions. With the development of IoT technologies and possibility of collecting high-precision and high-frequency data, even such vague concepts can be qualitatively measured, or at least approximated. In this paper, we demonstrate how the driver\u2019s degree of attention to the road can be automatically extracted from onboard sensor data. More specifically, our main contribution is introduction of a new metric, called attention horizon (AH); it can, fully automatically and based on readily-available IoT data, capture, differentiate, and evaluate a driver\u2019s behavior as the vehicle approaches a red traffic light. We suggest that our measure encapsulates complex concepts such as driver\u2019s \u201cawareness\u201d and \u201ccarefulness\u201d in itself. This metric is extracted from the pedal positions in a 150 m trajectory just before stopping. We demonstrate that this metric is correlated with normalized fuel consumption rate (FCR) in the long term, making it a suitable tool for ranking and evaluating drivers. For example, over weekly periods we found a negative median correlation between AH and FCR with the absolute value of 0.156; while using monthly data, the value was 0.402.<\/jats:p>","DOI":"10.3390\/s22062301","type":"journal-article","created":{"date-parts":[[2022,3,16]],"date-time":"2022-03-16T22:15:04Z","timestamp":1647468904000},"page":"2301","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Attention Horizon as a Predictor for the Fuel Consumption Rate of Drivers"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7209-9623","authenticated-orcid":false,"given":"Hamid","family":"Sarmadi","sequence":"first","affiliation":[{"name":"Center for Applied Intelligent Systems Research, Halmstad University, 302 50 Halmstad, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7796-5201","authenticated-orcid":false,"given":"S\u0142awomir","family":"Nowaczyk","sequence":"additional","affiliation":[{"name":"Center for Applied Intelligent Systems Research, Halmstad University, 302 50 Halmstad, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rune","family":"Prytz","sequence":"additional","affiliation":[{"name":"Stratio Company, 1050-127 Lisbon, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1480-976X","authenticated-orcid":false,"given":"Miguel","family":"Sim\u00e3o","sequence":"additional","affiliation":[{"name":"Stratio Company, 1050-127 Lisbon, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,16]]},"reference":[{"key":"ref_1","unstructured":"Chang, M.F., Evans, L., Herman, R., and Wasielewski, P. 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