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Evaluation of Video Masked Autoencoders' Performance and Uncertainty Estimations for Driver Action and Intention Recognition
University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. Department of Data Analytics and Engineering, R&D, Volvo Car Corporation, Sweden. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0003-2135-6615
University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0003-2949-4123
University of Skövde, School of Informatics. University of Skövde, Informatics Research Environment. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0001-8884-2154
Department of Data Analytics and Engineering, R&D, Volvo Car Corporation, Sweden.
2024 (English)In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), IEEE, 2024, p. 7429-7437Conference paper, Published paper (Refereed)
Abstract [en]

Traffic fatalities remain among the leading death causes worldwide. To reduce this figure, car safety is listed as one of the most important factors. To actively support human drivers, it is essential for advanced driving assistance systems to be able to recognize the driver's actions and intentions. Prior studies have demonstrated various approaches to recognize driving actions and intentions based on in-cabin and external video footage. Given the performance of self-supervised video pre-trained (SSVP) Video Masked Autoencoders (VMAEs) on multiple action recognition datasets, we evaluate the performance of SSVP VMAEs on the Honda Research Institute Driving Dataset for driver action recognition (DAR) and on the Brain4Cars dataset for driver intention recognition (DIR). Besides the performance, the application of an artificial intelligence system in a safety-critical environment must be capable to express when it is uncertain about the produced results. Therefore, we also analyze uncertainty estimations produced by a Bayes-by-Backprop last-layer (BBB-LL) and Monte-Carlo (MC) dropout variants of an VMAE. Our experiments show that an VMAE achieves a higher overall performance for both offline DAR and end-to-end DIR compared to the state-of-the-art. The analysis of the BBB-LL and MC dropout models show higher uncertainty estimates for incorrectly classified test instances compared to correctly predicted test instances.

Place, publisher, year, edition, pages
IEEE, 2024. p. 7429-7437
Series
Proceedings IEEE Workshop on Applications of Computer Vision, ISSN 2472-6737, E-ISSN 2642-9381
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
URN: urn:nbn:se:his:diva-23540DOI: 10.1109/WACV57701.2024.00726ISBN: 979-8-3503-1893-7 (print)ISBN: 979-8-3503-1892-0 (electronic)OAI: oai:DiVA.org:his-23540DiVA, id: diva2:1828101
Conference
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), January 4-8, 2024, Waikoloha, Hawaii, USA
Funder
Vinnova, 2018-05012Available from: 2024-01-16 Created: 2024-01-16 Last updated: 2024-04-11Bibliographically approved

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Vellenga, KoenSteinhauer, H. JoeFalkman, Göran

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CiteExportLink to record
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