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• W2897571271 abstract "Advanced Driver Assistance Systems (ADAS) come equipped on most modern vehicles and are intended to assist the driver and enhance the driving experience through features such as lane keeping system and adaptive cruise control. However, recent studies show that few people utilize these features for several reasons. First, ADAS features were not common until recently. Second, most users are unfamiliar with these features and do not know what to expect. Finally, the interface for operating these features is not intuitive. To help drivers understand ADAS features, we present a conversational in-vehicle digital assistant that responds to drivers' questions and commands in natural language. With the system prototyped herein, drivers can ask questions or command using unconstrained natural language in the vehicle, and the assistant trained by using advanced machine learning techniques, coupled with access to vehicle signals, responds in real-time based on conversational context. Results of our system prototyped on a production vehicle are presented, demonstrating its effectiveness in improving driver understanding and usability of ADAS." @default.
• W2897571271 created "2018-10-26" @default.
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• W2897571271 date "2018-10-11" @default.
• W2897571271 modified "2023-10-18" @default.
• W2897571271 title "Adasa" @default.
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• W2897571271 doi "https://doi.org/10.1145/3242587.3242593" @default.
• W2897571271 hasPublicationYear "2018" @default.
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