Detection of Active Emergency Vehicles using Per-Frame CNNs and Output Smoothing

• URL: http://arxiv.org/abs/2212.13696v1
• Date: Wed, 28 Dec 2022 04:45:51 GMT
• Title: Detection of Active Emergency Vehicles using Per-Frame CNNs and Output Smoothing
• Authors: Meng Fan, Craig Bidstrup, Zhaoen Su, Jason Owens, Gary Yang, Nemanja Djuric
• Abstract summary: 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. 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 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.
• Score: 4.917229375785646
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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