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Adasa: A Conversational In-Vehicle Digital Assistant for Advanced Driver Assistance Features.

, , , , , , and . UIST, page 531-542. ACM, (2018)

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Towards In-Vehicle Multi-Task Facial Attribute Recognition: Investigating Synthetic Data and Vision Foundation Models., and . CoRR, (2024)Driver fatigue transition prediction in highly automated driving using physiological features., , , , , , , , and . Expert Syst. Appl., (2020)Predicting Driver Fatigue in Automated Driving with Explainability., , , , , , , and . CoRR, (2021)Adasa: A Conversational In-Vehicle Digital Assistant for Advanced Driver Assistance Features., , , , , , and . UIST, page 531-542. ACM, (2018)One Agent Too Many: User Perspectives on Approaches to Multi-agent Conversational AI., , , , , and . CoRR, (2024)Study of Night Vision Configuration with Augmented Reality in Automotive Context., , , and . HCI (47), volume 1836 of Communications in Computer and Information Science, page 91-98. Springer, (2023)An Exploratory Study of Driver Response to Reduced System Confidence Notifications in Automated Driving., , , , , and . IEEE Trans. Intell. Veh., 1 (4): 325-334 (2016)Predicting Driver Fatigue in Monotonous Automated Driving with Explanation using GPBoost and SHAP., , , , , , , and . Int. J. Hum. Comput. Interact., 38 (8): 719-729 (2022)One Agent To Rule Them All: Towards Multi-agent Conversational AI., , , , , , , , and . ACL (Findings), page 3258-3267. Association for Computational Linguistics, (2022)Looking ahead: Anticipatory interfaces for driver-automation collaboration., , , , and . ITSC, page 1-7. IEEE, (2017)