This thesis addresses the problem of provident vehicle detection at night: The detection of oncoming vehicles by detecting its light artifacts. In particular, this means detecting a vehicle is oncoming even if it is not directly visible. For this purpose, a field study is conducted to understand human provident detection abilities and to record data. With the recorded data, a dataset is derived that can be used to analyze the performance of computer vision algorithms and to train machine learning approaches to detect vehicles providently. Furthermore, a complete computer vision algorithm is designed and trained on the derived dataset to detect light artifacts caused by the headlights of oncoming vehicles. The performance of this algorithm is compared with the measured performance of humans and an in-production computer vision system to demonstrate that the proposed system detects oncoming vehicles faster than humans and even faster than an in-production computer vision system. In addition, to show the usefulness of the developed algorithm, it is deployed in a test vehicle to use the detected light artifacts to control the glare-free high beam system proactively.weiterlesen