Autonomous Navigation in Urban Area using Spatio-Temporal Traffic Modeling


Autonomous Navigation in Urban Area using Spatio-Temporal Traffic Modeling – We present a new technique for predicting future movements based on the spatial-temporal information of the environment. Our approach employs a Convolutional Neural Network (CNN), to predict the appearance of the environment. With this new approach, the CNN can simultaneously learn to predict the current state and predict future states from a previous state, thus providing a direct representation to the environment. Based on the prediction, the CNN computes a prediction score based on the current state and a posterior distribution to estimate the future state. This gives the CNN a better model for predictability. We demonstrate the use of these spatial and temporal cues in several real-world applications. The proposed approach is a very promising candidate for future state prediction in traffic and autonomous vehicles.

This paper presents a new approach for object segmentation that leverages the deep learning of a multiscale sparse sparse model. This approach, called Convolutional Neural Network, is a hybrid multi-view CNN that uses both multiscale sparsity and sparse normal structures to train sparse representation and normalization tasks. It combines a fully automatic algorithm with a fully automatic multichannel regression algorithm. The results show that, in a non-convex nonlinear case, it outperforms the state-of-the-art on MNIST dataset and NIST dataset. Our method is also successful on datasets consisting of multiple object views of a single object with multiple views of different types of objects.

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Autonomous Navigation in Urban Area using Spatio-Temporal Traffic Modeling

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    Multi-View Sparse Subspace LearningThis paper presents a new approach for object segmentation that leverages the deep learning of a multiscale sparse sparse model. This approach, called Convolutional Neural Network, is a hybrid multi-view CNN that uses both multiscale sparsity and sparse normal structures to train sparse representation and normalization tasks. It combines a fully automatic algorithm with a fully automatic multichannel regression algorithm. The results show that, in a non-convex nonlinear case, it outperforms the state-of-the-art on MNIST dataset and NIST dataset. Our method is also successful on datasets consisting of multiple object views of a single object with multiple views of different types of objects.


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