Dataset for Robust and Accurate Leading Vehicle Velocity Recognition
This provides a dataset for researchers and developers in autonomous driving to improve recognition technology, but it is incremental as it focuses on data collection rather than new methods.
The authors tackled the problem of robust velocity recognition for leading vehicles in autonomous driving by constructing a dataset that includes challenging conditions like rainy weather and nighttime, making it available for benchmarking.
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