Visual vehicle surveillance has become an important research field due to its wide range of traffic applications. This task becomes more relevant in nighttime because accidents considerably increase. Typically, this problem is addressed by segmenting the bright image regions produced by vehicle lights, assuming they are well defined. But, often there are only flashes that occupy large image regions, invalidating the previous strategy. Thus, a real-time vehicle detection algorithm for nighttime that addresses the previous challenge is presented. First, the whole image is characterized by only one descriptor. Then, a grid of foveal classifiers that share the same previous image descriptor (unlike the traditional sliding window scheme) estimates the vehicle positions. Every classifier is trained to detect vehicles in specific image regions by analyzing the complex light patterns in the night. Furthermore, a new nighttime database has been also created to assess the effectiveness of the proposed method.