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 present the development of the first neural-network-based fully convolutional reinforcement learning (CNN-RL) model, named R-CNN, which is a fully generative, adversarial, data-driven, multi-objective reinforcement learning (DRL). The RL model learns a non-parametric representation of the context on a set of items, which predicts the items’ behaviors. This representation is then used to perform reinforcement. We show that state-of-the-art CNN-RL models with state-of-the-art reinforcement learning (RLs) succeed in achieving good performance on the task of reinforcement learning, but they do not learn accurate prediction performance. We develop a novel learning algorithm, called Fast RL-R, that learns to predict the most valuable items for each item, by leveraging the ability of multiple representations. The model is shown to outperform RL-RL models that use only a few items in the training data.

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Autonomous Navigation in Urban Area using Spatio-Temporal Traffic Modeling

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    Interactive Parallel Inference for Latent Variable Models with Continuous SignalsWe present the development of the first neural-network-based fully convolutional reinforcement learning (CNN-RL) model, named R-CNN, which is a fully generative, adversarial, data-driven, multi-objective reinforcement learning (DRL). The RL model learns a non-parametric representation of the context on a set of items, which predicts the items’ behaviors. This representation is then used to perform reinforcement. We show that state-of-the-art CNN-RL models with state-of-the-art reinforcement learning (RLs) succeed in achieving good performance on the task of reinforcement learning, but they do not learn accurate prediction performance. We develop a novel learning algorithm, called Fast RL-R, that learns to predict the most valuable items for each item, by leveraging the ability of multiple representations. The model is shown to outperform RL-RL models that use only a few items in the training data.


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