Pedestrian lane detection is an important task in many assistive and autonomous navigation systems. This article presents a new approach for pedestrian lane detection in unstructured environments, where the pedestrian lanes can have arbitrary surfaces with no painted markers. In this approach, a hybrid deep learning-Gaussian process (DL-GP) network is proposed to segment a scene image into lane and background regions. The network combines a compact convolutional encoder-decoder net and a powerful nonparametric hierarchical GP classifier. The resulting network with a smaller number of trainable parameters helps mitigate the overfitting problem while maintaining the modeling power. In addition to the segmentation output for each test image, the network also generates a map of uncertainty - a measure that is negatively correlated with the confidence level with which we can trust the segmentation. This measure is important for pedestrian lane-detection applications, since its prediction affects the safety of its users. We also introduce a new data set of 5000 images for training and evaluating the pedestrian lane-detection algorithms. This data set is expected to facilitate research in pedestrian lane detection, especially the application of DL in this area. Evaluated on this data set, the proposed network shows significant performance improvements compared with several existing methods.