Pedestrian lane detection is a core component in many assistive and autonomous navigation systems. These systems are usually deployed on environments that require real-time processing. Many state-of-the-art deep neural networks only focus on detection accuracy but not inference speed. Hence, without further modifications, they are not suitable for real-time applications. Furthermore, the task of designing a high-performing deep neural network is time-consuming and requires experience. To tackle these issues, we propose a neural architecture search algorithm that can find the best deep network for pedestrian lane detection automatically. The proposed method searches in a network-level space using the gradient descent algorithm. Evaluated on a dataset of 5,000 images, the models derived by the proposed algorithm achieve comparable segmentation accuracy, while being significantly faster than other state-of-the-art methods. The proposed method has been successfully implemented as a real-time pedestrian lane detection tool.