{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,8]],"date-time":"2024-09-08T05:18:56Z","timestamp":1725772736954},"reference-count":0,"publisher":"Link\u00f6ping University Electronic Press","license":[{"start":{"date-parts":[[2024,6,14]],"date-time":"2024-06-14T00:00:00Z","timestamp":1718323200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>Safe overtakes in trucks are crucial to prevent accidents, reduce congestion, and ensure efficient traffic flow, making early prediction essential for timely and informed driving decisions. Accordingly, we investigate the detection of truck overtakes from CAN data. Three classifiers, Artificial Neural Networks (ANN), Random Forest, and Support Vector Machines (SVM), are employed for the task. Our analysis covers up to 10 seconds before the overtaking event, using an overlapping sliding window of 1 second to extract CAN features. We observe that the prediction scores of the overtake class tend to increase as we approach the overtake trigger, while the no-overtake class remain stable or oscillates depending on the classifier. Thus, the best accuracy is achieved when approaching the trigger, making early overtaking prediction challenging. The classifiers show good accuracy in classifying overtakes (Recall\/TPR \u2265 93%), but accuracy is suboptimal in classifying no-overtakes (TNR typically 80-90% and below 60% for one SVM variant). We further combine two classifiers (Random Forest and linear SVM) by averaging their output scores. The fusion is observed to improve no-overtake classification (TNR \u2265 92%) at the expense of reducing overtake accuracy (TPR). However, the latter is kept above 91% near the overtake trigger. Therefore, the fusion balances TPR and TNR, providing more consistent performance than individual classifiers.<\/jats:p>","DOI":"10.3384\/ecp208018","type":"proceedings-article","created":{"date-parts":[[2024,6,14]],"date-time":"2024-06-14T11:54:55Z","timestamp":1718366095000},"page":"160-167","source":"Crossref","is-referenced-by-count":0,"title":["Predicting Overtakes In Trucks Using Can Data"],"prefix":"10.3384","volume":"208","author":[{"given":"Talha Hanif","family":"Butt","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Prayag","family":"Tiwari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fernando","family":"Alonso-Fernandez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1959","published-online":{"date-parts":[[2024,6,14]]},"event":{"name":"14th Scandinavian Conference on Artificial Intelligence SCAI 2024, June 10-11, 2024, J\u00f6nk\u00f6ping, Sweden","acronym":"SCAI 2024"},"container-title":["Link\u00f6ping Electronic Conference Proceedings","14th Scandinavian Conference on Artificial Intelligence SCAI 2024, June 10-11, 2024, J\u00f6nk\u00f6ping, Sweden"],"original-title":[],"link":[{"URL":"https:\/\/ecp.ep.liu.se\/index.php\/sais\/article\/download\/1010\/918","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ecp.ep.liu.se\/index.php\/sais\/article\/download\/1010\/918","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,14]],"date-time":"2024-06-14T11:54:59Z","timestamp":1718366099000},"score":1,"resource":{"primary":{"URL":"https:\/\/ecp.ep.liu.se\/index.php\/sais\/article\/view\/1010"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,14]]},"references-count":0,"URL":"https:\/\/doi.org\/10.3384\/ecp208018","relation":{},"ISSN":["1650-3686"],"issn-type":[{"type":"print","value":"1650-3686"}],"subject":[],"published":{"date-parts":[[2024,6,14]]}}}