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- W4313305619 abstract "While inferring common actor states (such as position or velocity) is an important and well-explored task of the perception system aboard a self-driving vehicle (SDV), it may not always provide sufficient information to the SDV. This is especially true in the case of active emergency vehicles (EVs), where light-based signals also need to be captured to provide a full context. We consider this problem and propose a sequential methodology for the detection of active EVs, using an off-the-shelf CNN model operating at a frame level and a downstream smoother that accounts for the temporal aspect of flashing EV lights. We also explore model improvements through data augmentation and training with additional hard samples." @default.
- W4313305619 created "2023-01-06" @default.
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- W4313305619 date "2022-12-27" @default.
- W4313305619 modified "2023-09-27" @default.
- W4313305619 title "Detection of Active Emergency Vehicles using Per-Frame CNNs and Output Smoothing" @default.
- W4313305619 doi "https://doi.org/10.48550/arxiv.2212.13696" @default.
- W4313305619 hasPublicationYear "2022" @default.
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