论文标题
使用人均CNN和输出平滑检测活动的紧急车辆
Detection of Active Emergency Vehicles using Per-Frame CNNs and Output Smoothing
论文作者
论文摘要
虽然推断普通演员状态（例如位置或速度）是自动驾驶工具（SDV）的感知系统的一项重要且经过充分探索的任务，但它可能并不总是向SDV提供足够的信息。在主动应急车辆（EVS）的情况下，尤其如此，在此也需要捕获基于光的信号以提供完整的环境。我们考虑了这个问题，并提出了一种用于检测活动电动汽车的顺序方法，使用在框架级别上运行的现成的CNN模型，以及一个说明闪烁电动汽车灯的时间方面的下游更平滑的方法。我们还通过使用其他硬样品来探索模型改进和培训。
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.
