Road detection is of high importance in different advanced driver-assistance systems. It is widely used for functionalities such as pedestrian detection, obstacle avoidance, autonomous navigation, etc. The vision-based road detection is to classify image pixels belonging to road surface or not. Up to now, most road detection algorithms are designed for working during daytime. In this paper, we mainly focus on road detection at night. A near-infrared camera which provides infrared lamps to strengthen the weak illumination is used for image capturing. Firstly a planar reflection model is proposed to fit the intensity distribution of the images pixels. Next, a pixel-based classification is applied to determine whether the pixel is on the road surface or not. In the experiments, we compare our algorithm with the region growing method. The experiments show that our approach works better in some aspects.