Scene understanding is a core issue for autonomous vehicles. However, its implementation has been thwarted by various outstanding issues, such as understanding forest roads in unknown field environments. Traditional three-dimensional (3D) point clouds or 3D estimation of fused data consume large amounts of memory and energy, making these methods less reliable in new energy vehicles with limited computational, memory, and energy resources. In this study, we propose a lightweight method to understand forest roads using a low-cost monocular camera. We extracted and clustered spatially similar texture projections based on oblique effect. Through the relative relationship between vanishing points and texture projections, contour lines can be estimated. After that, searching for the corresponding supporting evidence lines, we can segment the surface of the forest road, which can provide a decision basis for the automatic driving control system of new energy vehicles with limited resources. Unlike deep learning methods that are extremely resource-consuming, the proposed method requires no prior training, no calibration, and no internal parameters of the camera. At the same time, pure geometric reasoning makes the method robust to the ever-changing colors and lighting in the forest environment. The percentage of correctly classified pixels is compared to the ground truth. The experimental results show that the method can successfully understand forest roads and meet the requirements of autonomous navigation in forest environments for new energy vehicles with limited resources.