[1] Wang Wenfeng, Ding Weili, Li Yong et al. An efficient road detection algorithm based on parallel edges[J]. Acta Optica Sinica, 35, 0715001(2015).

[2] Duan Zhigang, Li Yong, Wang Ende et al. Road and navigation line detection algorithm from shadow image based on the illumination invariant image[J]. Acta Optica Sinica, 36, 1215004(2016).

[3] Urtasun R, Lenz P, Geiger A. Are we ready for autonomous driving? The KITTI vision benchmark suite[C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3354-3361(2012).

[4] Mendes C C T, Fr Mont V, Wolf D F. Exploiting fully convolutional neural networks for fast road detection[C]. IEEE International Conference on Robotics and Automation (ICRA), 3174-3179(2016).

[5] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[C]. International Conference on Neural Information Processing Systems (NIPS), 25, 1097-1105(2012).

[6] Teichmann M, Weber M, Zoellner M et al. MultiNet: real-time joint semantic reasoning for autonomous driving[J]. Computer Vision and Pattern Recognition(2016). http://arxiv.org/abs/1612.07695

[7] Oliveira G L, Burgard W, Brox T. Efficient deep models for monocular road segmentation[C]. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 4885-4891(2016).

[8] Gupta S, Girshick R, Arbel Ez P et al. Learning rich features from RGB-D images for object detection and segmentation[C]. 13th European Conference on Computer Vision (ECCV), 345-360(2014).

[9] Chen X Z, Ma H M, Wan J et al[C]. Multi-view 3D object detection network for autonomous driving IEEE Conference on Computer Vision and Pattern Recognition, 2016.

[10] Hu Z, Uchimura K. U-V-disparity: an efficient algorithm for stereovision based scene analysis[C]. IEEE Intelligent Vehicles Symposium (IVS), 48-54(2005).

[11] Ojala T, Pietikainen M, Harwood D. Performance evaluation of texture measures with classification based on Kullback discrimination of distributions[C]. 12th International Conference on Pattern Recognition (ICPR), 582-585(1994).

[12] Nair V, Hinton G E. Rectified linear units improve restricted boltzmann machines[C]. 27th International Conference on Machine Learning (ICML), 807-814(2010).

[13] Srivastava N, Hinton G, Krizhevsky A et al. Dropout: a simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research, 15, 1929-1958(2014). http://dl.acm.org/citation.cfm?id=2670313&preflayout=flat

[14] Zbontar J, Lecun Y. Computing the stereo matching cost with a convolutional neural network[C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1592-1599(2015).

[15] Jia Y, Shelhamer E, Donahue J et al. Caffe: convolutional architecture for fast feature embedding[J]. Proceedings of the 22nd ACM International Conference on Multimedia, 675-678(2014). http://dl.acm.org/citation.cfm?id=2654889