Accurate speed estimation of road vehicles is important for several reasons. One is speed limit enforcement, which represents a crucial tool in decreasing traffic accidents and fatalities. Compared with other research areas and domains, the number of available datasets for vehicle speed estimation is still very limited. We present a dataset of on-road audio-video recordings of single vehicles passing by a camera at known speeds, maintained stable by the on-board cruise control. The dataset contains thirteen vehicles, selected to be as diverse as possible in terms of manufacturer, production year, engine type, power and transmission, resulting in a total of $ 400 $ annotated audio-video recordings. The dataset is fully available and intended as a public benchmark to facilitate research in audio-video vehicle speed estimation. In addition to the dataset, we propose a cross-validation strategy which can be used in a machine learning model for vehicle speed estimation. Two approaches to training-validation split of the dataset are proposed.

翻译：对公路车辆的准确速度估计很重要,原因如下:一是速度限制执行,这是减少交通事故和死亡的关键工具。与其他研究领域和领域相比,可用于车辆速度估计的数据集数量仍然非常有限。我们提供了一套由一台已知速度的相机以已知速度通过、由船上巡航控制保持稳定的单一车辆的现场录音录像数据集。数据集包含13部车辆,从制造商、生产年份、引擎类型、功率和传输角度选择了尽可能多样化的车辆,导致总共400美元的附加录音录音记录。数据集完全可用,并打算作为公共基准,以便利对音像车辆速度估计的研究。除了数据集外,我们提议了一个交叉校验战略,可用于车辆速度估计的机器学习模型。提出了两种对数据集进行训练验证的方法。