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Advanced Driving Assistance System (ADAS) can predict pedestrian's trajectory, in order to avoid traffic accidents and guarantee driving safety. A few current pedestrian trajectory prediction methods use a pedestrian's historical motion to predict the future trajectory, but the pedestrian's trajectory is also affected by the vehicle using the ADAS for prediction (target vehicle). Other studies predict the pedestrian's and vehicle's trajectories separately, and use the latter to adjust the former, but their interaction is a continuous process and should be considered during prediction rather than after. Therefore, we propose PVII, a pedestrian-vehicle interactive and iterative prediction framework for pedestrian's trajectory. It makes prediction for one iteration based on the results from previous iteration, which essentially models the vehicle-pedestrian interaction. In this iterative framework, to avoid accumulation of prediction errors along with the increased iterations, we design a bi-layer Bayesian en/decoder. For each iteration, it not only uses inaccurate results from previous iteration but also accurate historical data for prediction, and calculates Bayesian uncertainty values to evaluate the results. In addition, the pedestrian's trajectory is affected by both target vehicle and other vehicles around it (surrounding vehicle), so we include into the framework a pre-trained speed estimation module for surrounding vehicles (SE module). It estimates the speed based on pedestrian's motion and we collect data from pedestrian's view for training. In experiments, PVII can achieve the highest prediction accuracy compared to the current methods. [ABSTRACT FROM AUTHOR]

Time series forecasting is ubiquitous in various scientific and industrial domains. Powered by recurrent and convolutional and self-attention mechanism, deep learning exhibits high efficacy in time series forecasting. However, the existing forecasting methods are suffering some limitations. For example, recurrent neural networks are limited by the gradient vanishing problem, convolutional neural networks cost more parameters, and self-attention has a defect in capturing local dependencies. What's more, they all rely on time invariant or stationary since they leverage parameter sharing by repeating a set of fixed architectures with fixed parameters over time or space. To address the above issues, in this paper we propose a novel time-variant framework named Self-Attention-based Time-Variant Neural Networks (SATVNN), generally capable of capturing dynamic changes of time series on different scales more accurately with its time-variant structure and consisting of self-attention blocks that seek to better capture the dynamic changes of recent data, with the help of Gaussian distribution, Laplace distribution and a novel Cauchy distribution, respectively. SATVNN obviously outperforms the classical time series prediction methods and the state-of-the-art deep learning models on lots of widely used real-world datasets. [ABSTRACT FROM AUTHOR]