Provident Vehicle Detection at Night for Advanced Driver Assistance Systems
This addresses a safety-critical limitation in advanced driver assistance systems for autonomous driving by mimicking human anticipatory behavior.
The paper tackles the problem of detecting oncoming vehicles at night by analyzing light artifacts from headlights, enabling provident detection before vehicles are directly visible, and demonstrates a time benefit compared to an in-production system.
In recent years, computer vision algorithms have become more powerful. However, current algorithms mainly share one limitation: They rely on directly visible objects. This is a significant drawback compared to human behavior, where visual cues caused by objects (e.g., shadows) are already used intuitively to retrieve information or anticipate occurring objects. While driving at night, this performance deficit becomes even more obvious: Humans already process the light artifacts caused by the headlamps of oncoming vehicles to estimate where they appear, whereas current object detection systems require that the oncoming vehicle is directly visible before it can be detected. Based on previous work on this subject, in this paper, we present a complete system that can detect light artifacts caused by the headlights of oncoming vehicles so that it detects that a vehicle is approaching providently. For that, an entire algorithm architecture is investigated, including the detection in the image space, the three-dimensional localization, and the tracking of light artifacts. To demonstrate the usefulness of such an algorithm, the proposed algorithm is deployed in a test vehicle to use the detected light artifacts to control the glare-free high beam system proactively. Using this experimental setting, the provident vehicle detection system's time benefit compared to an in-production computer vision system is quantified. Additionally, the glare-free high beam use case provides a real-time and real-world visualization interface of the detection results by considering the adaptive headlamps as projectors. With this investigation of provident vehicle detection, we want to put awareness on the unconventional sensing task of detecting objects providently and further close the performance gap between human behavior and computer vision algorithms to bring autonomous and automated driving a step forward.