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Abstract
Understanding attention is crucial for improving safety in driving scenarios. Detected and classified objects, along with their observation by the driver, are used as a measure of attention. This paper investigates the differences between human and artificial attention in real-world and replay driving scenarios. By analyzing attention patterns from drivers and a vision-language model agent, we identify a number of differences. The results highlight the limitations of current AI attention models and suggest the way forward for developing more context-aware systems.
Originalsprache | englisch |
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Seitenumfang | 6 |
Publikationsstatus | Veröffentlicht - 7 Dez. 2024 |
Veranstaltung | International Workshop on Smart Moving (SMVG 2024): Co-located with ACM/IEEE Symposium on Edge Computing - Rome, Italy, Rome, Italien Dauer: 7 Dez. 2024 → 9 Dez. 2024 https://acm-ieee-sec.org/2024/interact_moving.php |
Workshop
Workshop | International Workshop on Smart Moving (SMVG 2024) |
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Kurztitel | SMVG 2024 |
Land/Gebiet | Italien |
Ort | Rome |
Zeitraum | 7/12/24 → 9/12/24 |
Internetadresse |
Fields of Expertise
- Information, Communication & Computing
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CORVETTE - Kognitive Sensorik für fahrzeugflottengesteuerte Datendienste
Saukh, O. (Teilnehmer (Co-Investigator)), Römer, K. U. (Teilnehmer (Co-Investigator)), Krisper, M. (Teilnehmer (Co-Investigator)) & Papst, F. (Teilnehmer (Co-Investigator))
1/05/21 → 31/03/25
Projekt: Forschungsprojekt