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.

State-of-the-art deep CNNs are characterized by a high number of feature vector representations that are used to train a single model model for a given task. Moreover, a wide variety of tasks in artificial and real life applications can be learned simultaneously with a single deep model. In this paper, we propose a novel approach for jointly learning features and deep networks by using joint representations of different dimensions such as the convolutional, convolutional, or multi-dimensional. Unlike traditional CNNs, which only learn the features in the convolutional layers, we can learn the features on the convolutional layers without any prior knowledge about the data of interest. We demonstrate that the proposed approach outperforms the state-of-the-art deep CNNs on several benchmark datasets that are difficult to be trained by traditional CNNs.

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Autonomous Navigation in Urban Area using Spatio-Temporal Traffic Modeling

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