PVDN-Urban - A Dataset for Provident Vehicle Detection at Night in Urban Scenarios

For an autonomous vehicle to drive safely and efficiently, it is important to have a good understanding of the surrounding environment. Having early information about other road users can enable the vehicle to anticipate their behavior and take appropriate actions to avoid dangerous situations. Humans often use light reflections caused by oncoming vehicles at night to anticipate their appearance before they are directly visible. This problem of provident vehicle detection at night has already been studied in rural land road scenarios. However, urban scenarios are more complex, as the number of light sources is higher and the light reflections are more complex. In this paper, we therefore present the PVDN-urban dataset, which is the first dataset to study provident vehicle detection at night in urban scenarios. The dataset contains detailed annotations of light reflections caused by oncoming vehicles at night in urban scenarios. Also, it provides bounding box annotations for all vehicles, to make the dataset usable also for conventional vehicle detection tasks under low-light conditions. We provide an in-depth analysis of the dataset, an efficient annotation method for light reflections, as well as baseline results using state-of-the-art semantic segmentation models. With that, we provide the basis to further study provident vehicle detection at night also for complex urban scenarios. Furthermore, we provide a dataset for the development of algorithms for general vehicle detection at night in urban scenarios.