Autonomous driving paper index
Multimodal-context Interruptibility Dataset for Proactive Services on Smart Speakers at Home
One-line summary
To fill this gap, we present INPROSH, a dataset collected from 26 participants over a three-week in-the-wild field study.
Engineering notes
Key topics: autonomous driving. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
Abstract As smart speakers become increasingly integrated into everyday home life, there is growing interest in leveraging these speakers to proactively deliver personalized proactive services. To enhance user experience and engagement of proactive services, a key challenge is identifying opportune moments, user contexts when users are most interruptible to engage in proactive services. Researchers have identified such moments by exploring interruptibility across various user contexts (in which contexts users are more interruptible), and several datasets have been released with interruptibility labels. However, no publicly available dataset has focused specifically on interruptibility within home environments. To fill this gap, we present INPROSH, a dataset collected from 26 participants over a three-week in-the-wild field study. Participants used proactive services via smart speakers in their homes. INPROSH comprises 2,830 cases, each annotated with interruptibility labels and enriched with contextual information, including temporal, spatial, and behavioral contexts, as well as surrounding image and sound recordings near the smart speakers. We believe INPROSH will support a deeper understanding and more accurate detection of interruptibility in home environments.
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