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Pedestrian detection has always been a research hotspot in the Advanced Driving Assistance System (ADAS) with great progress in recent years. However, for the ADAS, we not only need to detect the behavior of pedestrians in front of the vehicle but also predict future action and the motion trajectory. Therefore, in this paper, we propose a human key point combined optical flow network (KPOF-Net) in the vehicle ADAS for the occlusion situation in the actual scene. When the vehicle encounters a blocked pedestrian at a traffic intersection, we used self-flow to estimate the global optical flow in the image sequence and then proposed a White Edge Cutting (WEC) algorithm to remove obstructions and simply modified the generative adversarial network to initialize pedestrians behind the obstructions. Next, we extracted pedestrian optical flow information and human joint point information in parallel, among which we trained four human key point models suitable for traffic intersections. At last, KPOF-GPDM fusion was proposed to predict the future status and walking trajectories of pedestrians, which combined optical flow information with human key point information. In the experiment, we did not merely compare our method with other four representative approaches in the same scene sequences. We also verified the accuracy of the pedestrian motion state and motion trajectory prediction of the system after fusion of human joint points and optical flow information. Taking into account the real-time performance of the system, in the low-speed and barrier-free environment, the comparative analysis only uses optical flow information, human joint point information, and KPOF-Net three prediction models. The results show that (1) in the same traffic environment, our proposed KPOF-Net can predict the change of pedestrian motion state about 5 frames (about 0.26s) ahead of other excellent systems; (2) at the same time, our system predicts the trajectory of the pedestrian more accurately than the other four systems, which can achieve more stable minimum error ±0.04m; (3) in a low-speed, barrier-free experimental environment, our proposed trajectory prediction model that integrates human joint points and optical flow information has higher prediction accuracy and smaller fluctuations than a single-information prediction model, and it can be well applied to automobiles' ADAS., Author(s): Xudong Long [1]; Weiwei Zhang (corresponding author) [2]; Bo Zhao [1]; Shaoxing Mo [1] 1. Introduction In the automatic driving scene, efficient detection of vehicles and pedestrians around the [...]

Identification of a groundwater contaminant source simultaneously with the hydraulic conductivity in highly heterogeneous media often results in a high‐dimensional inverse problem. In this study, a deep autoregressive neural network‐based surrogate method is developed for the forward model to allow us to solve efficiently such high‐dimensional inverse problems. The surrogate is trained using limited evaluations of the forward model. Since the relationship between the time‐varying inputs and outputs of the forward transport model is complex, we propose an autoregressive strategy, which treats the output at the previous time step as input to the network for predicting the output at the current time step. We employ a dense convolutional encoder‐decoder network architecture in which the high‐dimensional input and output fields of the model are treated as images to leverage the robust capability of convolutional networks in image‐like data processing. An iterative local updating ensemble smoother algorithm is used as the inversion framework. The proposed method is evaluated using a synthetic contaminant source identification problem with 686 uncertain input parameters. Results indicate that, with relatively limited training data, the deep autoregressive neural network consisting of 27 convolutional layers is capable of providing an accurate approximation for the high‐dimensional model input‐output relationship. The autoregressive strategy substantially improves the network's accuracy and computational efficiency. The application of the surrogate‐based iterative local updating ensemble smoother in solving the inverse problem shows that it can achieve accurate inversion results and predictive uncertainty estimates. Plain Language Summary: High‐dimensional inverse problems are often computationally expensive since a large number of forward model evaluations are usually required. A computationally efficient alternative is to replace in the inversion process the forward model with an accurate but fast‐to‐evaluate surrogate model. However, most existing surrogate methods suffer from the "curse of dimensionality." In this paper, we develop a deep autoregressive neural network‐based surrogate method to efficiently solve such high‐dimensional inverse problems. The autoregressive neural network can efficiently obtain accurate approximations for the time‐dependent outputs of forward models with time‐varying inputs. In addition, a dense convolutional network architecture is employed to transform the surrogate modeling task to an image‐to‐image regression problem by leveraging the robust capability of convolutional networks in image‐like data processing. The curse of dimensionality was tackled though a series of inherent nonlinear projections of the input into low‐dimensional latent spaces. It is shown that the proposed network provides an accurate surrogate of a transport system with high‐dimensional uncertain inputs using relatively limited training data. Reliable inversion results are then obtained with a rather minor computational cost by using the surrogate to replace the forward model in the inversion process. Key Points: A deep neural network surrogate approach is developed for groundwater contaminant transport in highly heterogeneous mediaThe time‐varying process is captured using an autoregressive modelA 686‐dimensional inverse problem of contaminant source identification in heterogeneous media is addressed [ABSTRACT FROM AUTHOR]

Surrogate strategies are used widely for uncertainty quantification of groundwater models in order to improve computational efficiency. However, their application to dynamic multiphase flow problems is hindered by the curse of dimensionality, the saturation discontinuity due to capillarity effects, and the time dependence of the multi‐output responses. In this paper, we propose a deep convolutional encoder‐decoder neural network methodology to tackle these issues. The surrogate modeling task is transformed to an image‐to‐image regression strategy. This approach extracts high‐level coarse features from the high‐dimensional input permeability images using an encoder and then refines the coarse features to provide the output pressure/saturation images through a decoder. A training strategy combining a regression loss and a segmentation loss is proposed in order to better approximate the discontinuous saturation field. To characterize the high‐dimensional time‐dependent outputs of the dynamic system, time is treated as an additional input to the network that is trained using pairs of input realizations and of the corresponding system outputs at a limited number of time instances. The proposed method is evaluated using a geological carbon storage process‐based multiphase flow model with a 2,500‐dimensional stochastic permeability field. With a relatively small number of training data, the surrogate model is capable of accurately characterizing the spatiotemporal evolution of the pressure and discontinuous CO2 saturation fields and can be used efficiently to compute the statistics of the system responses. Key Points: A surrogate model for uncertainty quantification of dynamic multiphase flows is developed using deep convolutional encoder‐decoder networksA two‐stage network training strategy is proposed to improve the approximation of saturation discontinuityThe method can accurately predict the time‐dependent output fields of a multiphase flow model with a 2,500‐dimensional permeability field [ABSTRACT FROM AUTHOR]