Detecting vehicles in the dark in urban environments - A human benchmark

Abstract

When developing autonomous or automated driving functions, having a sound understanding of the environment is critical. The earlier and the more detailed the environment is recognized, the more precise the vehicle can plan future actions. One major component in the field of autonomous driving, therefore, is visual perception. Cameras deliver consecutive images of the world, and computer vision algorithms attempt to extract the relevant information, for example, detecting other road users such as vehicles. However, current automated driving functions share the limitation of relying on the object to be directly visible to be detected. On the other side, humans intuitively use other visual effects to draw assumptions about objects that are not yet directly visible. Such effects can be shadows during the day or light reflections during the night. Recent work has already shown the potential of using this information when driving at night on highways. Highways at night thereby are easy ground: The traffic volume is considerably low, and the surrounding environment is mainly dark (e. g., there are no street lamps causing other light sources or reflections). This paper takes the research on this topic a step further and brings light to the question to which extent this effect of detecting oncoming vehicles at night can also be used in cities. We present a comprehensive analysis of human behavior when detecting other road users at night in urban areas while driving. The data was gathered throughout a test group study in two medium-sized German cities under various weather and traffic conditions. We prove the importance of light reflections when driving through urban areas at night and provide a solid human benchmark to compare future perception algorithms’ performance.