Numerous groups have applied a variety of deep learning techniques to computer vision problems in highway perception scenarios. In this paper, we presented a number of empirical evaluations of recent deep learning advances. Computer vision, combined with deep learning, has the potential to bring about a relatively inexpensive, robust solution to autonomous driving. To prepare deep learning for industry uptake and practical applications, neural networks will require large data sets that represent all possible driving environments and scenarios. We collect a large data set of highway data and apply deep learning and computer vision algorithms to problems such as car and lane detection. We show how existing convolutional neural networks (CNNs) can be used to perform lane and vehicle detection while running at frame rates required for a real-time system. Our results lend credence to the hypothesis that deep learning holds promise for autonomous driving.
@cite_5: Predicting depth is an essential component in understanding the 3D geometry of a scene. While for stereo images local correspondence suffices for estimation, finding depth relations from a single image is less straightforward, requiring integration of both global and local information from various cues. Moreover, the task is inherently ambiguous, with a large source of uncertainty coming from the overall scale. In this paper, we present a new method that addresses this task by employing two deep network stacks: one that makes a coarse global prediction based on the entire image, and another that refines this prediction locally. We also apply a scale-invariant error to help measure depth relations rather than scale. By leveraging the raw datasets as large sources of training data, our method achieves state-of-the-art results on both NYU Depth and KITTI, and matches detailed depth boundaries without the need for superpixelation.
In contrast to these object detectors, we do not use any road or motion-based models; instead we rely only on the robustness of a neural network to make reasonable predictions. In addition, we currently do not rely on any temporal features, and the detector operates independently on single frames from a monocular camera. To make up for the lack of other sensors, which estimate object depth, we train the neural network to predict depth based on labels extracted from radar returns. Although the model only predicts a single depth value for each object, Eigen have shown how a neural network can predict entire depth maps from single images @cite_5 . The network we train likely learns some model of the road for object detection and depth predictions, but it is never explicitly engineered and instead learns from the annotations alone.