With the rapid development of sensor technologies, time series data collected by multiple and spatially distributed sensors have been widely used in different research fields. Examples of such data include geo-tagged temperature data collected by temperature sensors, air pollutant monitoring data, and traffic data collected by road traffic sensors. Due to sensor failure, communication errors and storage loss, etc., data collected by sensors inevitably includes missing data. However, models commonly used in the analysis of such large-scale data often rely on complete data sets. This paper proposes a model for the imputation of missing data of traffic flow, which combines a self-attention mechanism, an auto-encoder, and a generative adversarial network, into a self-attention generative adversarial imputation net (SA-GAIN). The introduction of the self-attention mechanism can help the proposed model to effectively capture correlations between spatially-distributed sensors at different time points. Adversarial training through two neural networks, called generators and discriminators, allows the proposed model to generate imputed data close to the real data. In comparison with different imputation models, the proposed model shows the best performance in imputing missing data.

This paper proposes an improved intelligent driver model (IDM) by considering the information of multiple front and rear vehicles to describe the car-following behaviour of CAVs (Connected and autonomous vehicles). The model involves the velocity and acceleration of multiple front and rear vehicles as well as the velocity difference and headway between the host vehicle and its surrounding vehicles. By introducing location-related parameters, the model quantitatively expresses the change in influence degree of a surrounding vehicle with its location to the host vehicle. To maximize traffic stability, we obtain the optimal value of the parameters in the model and the effect of specific time delays on the stability of traffic flow with numerical simulation. The results indicate that for a single vehicle control, the proposed model provides a much quicker and smoother acceleration and deceleration process to the desired speed than the IDM and multi-front IDM. And for fleet control, the proposed multi-front and rear IDM is superior to the other two models in decreasing the starting and braking time and increasing the stability of speed and acceleration. With effective car-following behaviour control, it is helpful to improve the operation efficiency of CAVs and enhance the stability of traffic flow. In addition to the car-following behaviour control, the model can be utilized for fleet control in the case of CAVs’ homogeneous flow. This model can also serve as an effective tool to simulate car-following behaviour, which is beneficial for road traffic management and infrastructure layout in connected environments.

Self-attention mechanism, which has been successfully applied to current encoder-decoder framework of image captioning, is used to enhance the feature representation in the image encoder and capture the most relevant information for the language decoder. However, most existing methods will assign attention weights to all candidate vectors, which implicitly hypothesizes that all vectors are relevant. Moreover, current self-attention mechanisms ignore the intra-object attention distribution, and only consider the inter-object relationships. In this paper, we propose a Multi-Gate Attention (MGA) block, which expands the traditional self-attention by equipping with additional Attention Weight Gate (AWG) module and Self-Gated (SG) module. The former constrains the attention weights to be assigned to the most contributive objects. The latter is adopted to consider the intra-object attention distribution and eliminate the irrelevant information in object feature vector. Furthermore, most current image captioning methods apply the original transformer designed for natural language processing task, to refine image features directly. Therefore, we propose a pre-layernorm transformer to simplify the transformer architecture and make it more efficient for image feature enhancement. By integrating MGA block with pre-layernorm transformer architecture into the image encoder and AWG module into the language decoder, we present a novel Multi-Gate Attention Network (MGAN). The experiments on MS COCO dataset indicate that the MGAN outperforms most of the state-of-the-art, and further experiments on other methods combined with MGA blocks demonstrate the generalizability of our proposal.

The Journal of Electronic Imaging (JEI), copublished bimonthly with the Society for Imaging Science and Technology, publishes peer-reviewed papers that cover research and applications in all areas of electronic imaging science and technology.

In complex terrain, such as uneven roads or irregular terrain, two-wheeled heavy-duty self-balancing vehicles are easily affected by external interference factors, causing rollover or rendering the vehicle unable to move, which poses a greater challenge to its stability control. Therefore, it is necessary to establish a kinematic model of a two-wheeled vehicle and design a control system to study its driving stability. This paper aims to study the stability control system of a two-wheeled self-balancing vehicle under complex terrain. First, a self-balancing vehicle modeling method based on complex terrain is designed. By analyzing the motion characteristics of the self-balancing vehicle, a kinematic model suitable for complex terrain is established, which provides a basis for subsequent control algorithms. Secondly, a precise control system is designed for different terrain conditions, and parameters such as vehicle attitude, speed and acceleration are adjusted through the Proportional–Integral–Derivative (PID) control algorithm to achieve the smooth operation of the self-balancing vehicle in complex terrain. In addition, a vehicle-mounted camera is used to capture terrain images in real time, and different terrains are accurately identified through the terrain recognition algorithm based on deep learning, thereby determining the friction coefficient and effectively improving the stability of the self-balancing vehicle on complex terrain. The experimental results show that the designed control system can enable the self-balancing two-wheeled vehicle to achieve stable balance control in different terrains, and has good applicability and stability.