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In time-varying fading channels, channel coefficients are estimated using pilot symbols that are transmitted every coherence interval. For channels with high Doppler spread, the rapid channel variations over time will require considerable bandwidth for pilot transmission, leading to poor throughput. In this paper, we propose a novel receiver architecture using deep recurrent neural networks (RNNs) that learns the channel variations and thereby reduces the number of pilot symbols required for channel estimation. Specifically, we design and train an RNN to learn the correlation in the time-varying channel and predict the channel coefficients into the future with good accuracy over a wide range of Dopplers and signal-to-noise ratios (SNR). The proposed training methodology enables accurate channel prediction through the use of techniques such as teacher-force training, early-stop, and reduction of learning rate on plateau. Also, the robustness of prediction for different Dopplers and SNRs is achieved by adapting the number of predictions into the future based on the Doppler and SNR. Numerical results show that good bit error performance is achieved by the proposed receiver in time-varying fading channels. We also propose a data decision driven receiver architecture using RNNs that further reduces the pilot overhead while maintaining good bit error performance. [ABSTRACT FROM AUTHOR]

Rear-end collision prediction has gained an increasing attention for safety improvement in smart cities. It is urgent to design efficient warning strategies for rear-end collisions which is one of the main causes of traffic accidents. The existing researches have been conducted to predict the collisions. Learning-based methods are proposed to solve this complicated issue, which the traditional methods are difficult to solve. However, due to some limitations in terms of the feature extraction and prediction performance, back-propagation learning methods are facing challenge. In this paper, we proposed a novel Rear-end Collision Prediction Mechanism with deep learning method (RCPM), in which a convolutional neural network model is established. In RCPM, the dataset is smoothed and expanded based on genetic theory to alleviate the class imbalance problem. The preprocessed dataset is divided into training and testing sets as the input to train our convolutional neural network model. The experimental results show that compared with the Honda, Berkeley and multi-layer perception neural-network-based algorithms, RCPM effectively improves performance to predict rear-end collisions. [ABSTRACT FROM AUTHOR]

A wet road is slippery so vehicles often slow down their speed to increase the safety margin, thus usually reducing the average speed. This reduction in average speed may produce a chain reaction that shifts, extends, or amplifies a slowdown on downstream road segments. Conventional average-speed forecasting approaches are unable to respond to sudden chain reactions because these approaches do not consider the effect of weather factors and upstream road segments. Since accurate forecast of average speed can improve gas consumption, carbon dioxide emissions, and travel time, this paper proposes a short-term average-speed forecast and adjustment (ASFA) approach based on a study of prediction bias correlation among adjacent road segments and on weather factors, such as temperature, humidity, and rainfall. First, this approach applies an artificial neural network to predict the average speed of a road segment. Then, vehicles can monitor current traffic to calculate average speed via vehicular ad hoc networks (VANETs). Finally, the vehicles adjust the predicted average speed according to the observed average speed. Real traffic measurements and weather data are used for the evaluation of the proposed scheme and Civic Boulevard in Taipei City is selected as the prediction target. The results show that the proposed ASFA improves accuracy by 57.4% when compared with a hybrid approach on an urban street during rush hour. This paper estimates and simulates a case study by aggregating traffic data in 186 ms on Shi-Min Boulevard via the VANETs during rush hour. [ABSTRACT FROM PUBLISHER]