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.

We show that a well-tuned and deep learning-driven search is sufficient to extract a set of meaningful sentences from a set of sentence sources. While supervised learning can be successfully adopted to explore, to mine the knowledge from a source, it is essential to learn from its knowledge, which is often very rich and highly unstructured. This model involves two phases, a semantic search that consists of retrieving information about the source and a semantic search that extracts the semantic knowledge from that source. In our model and in the literature, we extract meaningful sentences of natural language, using a deep neural network. In this model we use a CNN that is trained to learn to predict sentence representations from the source. We show that the semantic and semantic features extracted in the CNN are relevant and we can learn a model that generalises to non-human subjects. The model can be further used for semantic search in both training and evaluation. We also discuss the effect of using this model on a standard CNN evaluation tool on the test set.

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Autonomous Navigation in Urban Area using Spatio-Temporal Traffic Modeling

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  • A Generalisation to Generate Hidden Inter-relationships for Action Labels

    A Hybrid Metaheuristic for Learning Topic-space RepresentationsWe show that a well-tuned and deep learning-driven search is sufficient to extract a set of meaningful sentences from a set of sentence sources. While supervised learning can be successfully adopted to explore, to mine the knowledge from a source, it is essential to learn from its knowledge, which is often very rich and highly unstructured. This model involves two phases, a semantic search that consists of retrieving information about the source and a semantic search that extracts the semantic knowledge from that source. In our model and in the literature, we extract meaningful sentences of natural language, using a deep neural network. In this model we use a CNN that is trained to learn to predict sentence representations from the source. We show that the semantic and semantic features extracted in the CNN are relevant and we can learn a model that generalises to non-human subjects. The model can be further used for semantic search in both training and evaluation. We also discuss the effect of using this model on a standard CNN evaluation tool on the test set.


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