论文标题
数据集可用于稳健，准确的领先车速识别
Dataset for Robust and Accurate Leading Vehicle Velocity Recognition
论文作者
论文摘要
使用摄像机对周围环境的识别是高级驾驶员辅助系统和自动驾驶中的重要技术，并且近年来，诸如深度学习之类的机器学习方法通常可以解决识别技术。机器学习需要用于学习和评估的数据集。为了在现实世界中开发强大的识别技术，除了正常的驾驶环境外，对于诸如多雨天气或夜间摄像机的环境中的数据是必不可少的。我们已经构建了一个数据集，该数据集可以对技术进行基准测试，以针对领先车辆的速度识别。对于先进的驾驶员辅助系统和自动驾驶，此任务是一项重要的任务。该数据集可从https://signate.jp/competitions/657获得
Recognition of the surrounding environment using a camera is an important technology in Advanced Driver-Assistance Systems and Autonomous Driving, and recognition technology is often solved by machine learning approaches such as deep learning in recent years. Machine learning requires datasets for learning and evaluation. To develop robust recognition technology in the real world, in addition to normal driving environment, data in environments that are difficult for cameras such as rainy weather or nighttime are essential. We have constructed a dataset that one can benchmark the technology, targeting the velocity recognition of the leading vehicle. This task is an important one for the Advanced Driver-Assistance Systems and Autonomous Driving. The dataset is available at https://signate.jp/competitions/657
