To use the benefits of Advanced Driver Assistance Systems (ADAS)-Tests in simulation and reality a new approach for using Augmented Reality (AR) in an automotive vehicle for testing ADAS is presented in this paper. Our procedure provides a link between simulation and reality and should enable a faster development process for future increasingly complex ADAS tests and future mobility solutions. Test fields for ADAS offer a small number of orientation points. Furthermore, these must be detected and processed at high vehicle speeds. That requires high computational power both for developing our method and its subsequent use in testing. Using image segmentation (IS), artificial intelligence (AI) for object recognition, and visual simultaneous localization and mapping (vSLAM), we aim to create a three-dimensional model with accurate information about the test site. It is expected that using AI and IS will significantly improve performance as computational speed and accuracy for AR applications in automobiles.

We present in this paper a novel framework and distributed control laws for the formation of multiple unmanned rotorcraft systems, be it single-rotor helicopters or multi-copters, with physical constraints and with inter-agent collision avoidance, in cluttered environments. The proposed technique is composed of an analytical distributed consensus control solution in the free space and an optimization based motion planning algorithm for inter-agent and obstacle collision avoidance. More specifically, we design a distributed consensus control law to tackle a series of state constraints that include but not limited to the physical limitations of velocity, acceleration and jerk, and an optimization-based motion planning technique is utilized to generate numerical solutions when the consensus control fails to provide a collision-free trajectory. Besides, a sufficiency condition is given to guarantee the stability of the switching process between the consensus control and motion planning. Finally, both simulation and real flight experiments successfully demonstrate the effectiveness of the proposed technique.

In this paper, the formation control problem for a multi-agent system is studied. Two new robust control algorithms for serial and parallel formations respectively are proposed, which take the constraints of limited field of view into consideration. Without the need for any global information, the only relative information required is distance and bearing angle, thus is easy to implement with onboard directional sensors. It is then demonstrated how complex formations can be realized by combining the proposed basic controllers. Finally, effectiveness of the proposed algorithms is illustrated by numerical examples.

Although the pick-up/drop-off (PUDO) strategy in carpooling offers the convenience of short-distance walking for passengers during boarding and disembarking, there is a noticeable hesitancy among commuters to adopt this travel method, despite its numerous benefits. Here, this paper establishes a tripartite evolutionary game theory (EGT) model to verify the evolutionary stability of choosing the PUDO strategy of drivers and passengers and offering subsidies strategy of carpooling platforms in carpooling system. The model presented in this paper serves as a valuable tool for assessing the dissemination and implementation of PUDO strategy and offering subsidies strategy in carpooling applications. Subsequently, an empirical analysis is conducted to examine and compare the sensitivity of the parameters across various scenarios. The findings suggest that: firstly, providing subsidies to passengers and drivers, along with deductions for drivers through carpooling platforms, is an effective way to promote wider adoption of the PUDO strategy. Then, the decision-making process is divided into three stages: initial stage, middle stage, and mature stage. PUDO strategy progresses from initial rejection to widespread acceptance among drivers in the middle stage and, in the mature stage, both passengers and drivers tend to adopt it under carpooling platform subsidies; the factors influencing the costs of waiting and walking times, as well as the subsidies granted to passengers, are essential determinants that require careful consideration by passengers, drivers, and carpooling platforms when choosing the PUDO strategy. Our work provides valuable insight into the PUDO strategy’s applicability and the declared results provide implications for traffic managers and carpooling platforms to offer a suitable incentive.

With the increasing number of space satellites, the demand for satellite communication (including maneuvering, command uploading and data downloading) has also grown significantly. However, the actual communication resources of ground station are relatively limited, which leads to an oversubscribed problem. How to make use of limited ground station resources to complete satellite communication requests more fully and efficiently in the strict visible time is the focus of satellite range scheduling research. This paper reviews and looks forward to the research on Satellite Range Scheduling Problem (SRSP). Firstly, SRSP is defined as the scheduling problem of establishing communication between satellites and ground stations, and the classification and development of SRSP are introduced. Then, this paper analyzes three common problem description models, and establishes a mathematical model based on the analysis of optimization objectives and constraints. Thirdly, this paper classifies and summarizes the common solving methods of SRSP, and analyzes their characteristics and application scenarios. Finally, combined with the work in this paper, the future research direction of SRSP is envisioned.

Space-time video super-resolution (STVSR) serves the purpose to reconstruct high-resolution high-frame-rate videos from their low-resolution low-frame-rate counterparts. Recent approaches utilize end-to-end deep learning models to achieve STVSR. They first interpolate intermediate frame features between given frames, then perform local and global refinement among the feature sequence, and finally increase the spatial resolutions of these features. However, in the most important feature interpolation phase, they only capture spatial-temporal information from the most adjacent frame features, ignoring modelling long-term spatial-temporal correlations between multiple neighbouring frames to restore variable-speed object movements and maintain long-term motion continuity. In this paper, we propose a novel long-term temporal feature aggregation network (LTFA-Net) for STVSR. Specifically, we design a long-term mixture of experts (LTMoE) module for feature interpolation. LTMoE contains multiple experts to extract mutual and complementary spatial-temporal information from multiple consecutive adjacent frame features, which are then combined with different weights to obtain interpolation results using several gating nets. Next, we perform local and global feature refinement using the Locally-temporal Feature Comparison (LFC) module and bidirectional deformable ConvLSTM layer, respectively. Experimental results on two standard benchmarks, Adobe240 and GoPro, indicate the effectiveness and superiority of our approach over state of the art.

In response to the impact of COVID-19, the manufacturing industry and academic industrial research have largely shifted to online or hybrid conference formats. The sudden change has posed challenges for researchers and teams to adapt. Based on the current state of online conferences, inadequate communication, disruptions during meetings, confusion and loss of meeting information, and difficulties in conducting online collaborations are observed. This paper presents a design of a real-time discussion board that combines online conferences and synchronous discussions to address the issues arising from remote collaborations in industrial research. The research demonstrates that synchronous discussions conducted within multi-team industrial collaboration teams with specific and diverse issues can better control the flow of meetings, enhance meeting efficiency, promote participant interaction and engagement, reduce information loss, and weaken the boundaries between online and offline collaboration.

In this paper, we present a sufficient condition for the exponential stability of a class of linear switched systems. As an application of this stability result, we establish an output-based adaptive distributed observer for a general linear leader system over a periodic jointly connected switching communication network, which extends the applicability of the output-based adaptive distributed observer from a marginally stable linear leader system to any linear leader system and from an undirected switching graph to a directed switching graph. This output-based adaptive distributed observer will be applied to solve the leader-following consensus problem for multiple double-integrator systems.