Detecting Oncoming Vehicles at Night in Urban Scenarios - An Annotation Proof-Of-Concept

Detecting oncoming vehicles at night as early as possible is important for highly automated driving and Advanced-Driver-Assistance-Systems (ADAS). The sooner objects are detected, the earlier autonomous systems can take them into consideration to plan more anticipatory and safe actions. Previous work showed that on rural land roads at night, oncoming vehicles can already be detected before they are actually directly visible. This is done based on their emitted light. However, no work exists on covering the problem for more complex scenarios such as urban areas in cities. In this paper, we present a new approach to annotate light reflections in urban scenarios caused by oncoming vehicles at night before they are directly visible. We revisit design decisions in previous work for rural land road scenarios and find several improvements. We propose a pipeline which takes relatively cheap-to-get, yet highly subjective human Bounding-Box (BB) annotations, and automatically turns them into normally expensive-to-get, more objective binary masks. In our annotation experiment, we show that labeling light reflections is far more challenging and complex than conventional objects. Also, we show that our method can improve inter-annotator agreement and filter out annotator subjectivity. We train several State-Of-The-Art (SOTA) neural networks for semantic segmentation to demonstrate that our annotations can be used to detect light reflections from oncoming vehicles in urban scenarios before they are directly visible.