AI agents are increasingly explored for supporting reflection and well-being. However, we know little about how AI agents participate in reflective practices, particularly through co-creation. We presented MIRA, a co-creative AI agent that engages users in transforming abstract squiggles into concrete drawings while offering reflective feedback. Through a three-group comparative study, we examine how AI-mediated co-creation shapes reflective experience. We found that MIRA operates as a scaffold that introduces external reflective perspectives, helping users reinterpret experiences and surface emotions through visual expression. These findings highlight how AI agents can foster engaging co-creative experiences that encourage reflection among university students, while providing insights for designing AI-human co-creation that can extend to broader populations.

Long-distance relationships (LDRs) struggle to sustain intimacy without physical touch. Existing mediated social touch systems rely on designer-authored haptic patterns, which limit opportunities for personalization and shared meaning-making. We present Onni, a haptic interface that lets couples collaboratively define and experience a shared library of haptic interactions. In Study 1, we conducted co-creation workshops (n = 20) to examine how couples negotiate and align meanings in haptic interactions. In Study 2, we deployed Onni in everyday routines (n = 6) to explore how these interactions are adopted, adapted, and ritualized. Our findings illustrate that couples co-create and personalize haptic interactions through continuous exploration, negotiation, and situational adaptation. By integrating a dyadic co-design approach, an end-user authoring interface for a shared action–feedback haptic repertoire, and a longitudinal view of how meanings evolve in everyday LDR routines, this work advances the understanding of haptic meaning-making as a collaboratively constructed and ritualized process. It offers concrete design implications for building personalized, evolving haptic systems that support intimacy in LDRs.

Based on the Job Demand-Resource theory, this study explores the relationship between technostress and turnover intention focusing on the mediating role of work engagement and the moderating role of leader competency. Three-wave survey data were collected from 634 Chinese real estate agents who work in an industry characterized by both heavy reliance on information and communication technology and concerning level of turnover rate. The results indicated that technostress is positively related to turnover intention and this relationship is mediated by work engagement. Moreover, leader competency attenuated the influence of technostress on engagement and the indirect effect of technostress on turnover intention through engagement. Theoretical and practical implications are discussed.

Connected and automated vehicles (CAVs) have emerged as a potential solution to the future challenges of developing safe, efficient, and eco-friendly transportation systems. However, CAV control presents significant challenges significant challenges due to the complexity of interconnectivity and co-ordination required among vehicles. Multi-agent reinforcement learning (MARL), which has shown notable advancements in addressing complex problems in autonomous driving, robotics, and human-vehicle interaction, emerges as a promising tool to enhance CAV capabilities. Despite its potential, there is a notable absence of current reviews on mainstream MARL algorithms for CAVs. To fill this gap, this paper offers a comprehensive review of MARL’s application in CAV control. The paper begins with an introduction to MARL, explaining its unique advantages in handling complex and multi-agent scenarios. It then presents a detailed survey of MARL applications across various control dimensions for CAVs, including critical scenarios such as platooning control, lane-changing, and unsignalized intersections. Additionally, the paper reviews prominent simulation platforms essential for developing and testing MARL algorithms for CAVs. Lastly, it examines the current challenges in deploying MARL for CAV control, including safety, communication, mixed traffic, and sim-to-real challenges. Potential solutions discussed include hierarchical MARL, decentralized MARL, adaptive interactions, and offline MARL. The work has been summarized in MARL_in_CAV_Control_Repository.

This paper introduces an innovative hybrid approach for estimating vehicle lateral velocity, merging mechanism-based methods with a Long Short-Term Memory (LSTM) neural network. Traditional estimation techniques, which are often susceptible to drift and inaccuracies due to parameter mismatches, fail to effectively adapt to varying driving conditions. Our proposed approach leverages the accuracy of mechanism-based estimates in specific scenarios to feed the LSTM network, creating a data-mechanism-driven solution. To overcome the inherent challenges of data-driven models, particularly concerning data quality and volume, our lateral velocity estimation model incorporates a feature extraction layer alongside a regression output layer. This architecture not only facilitates efficient parameter optimization within the feature extraction phase but also enables targeted retraining of the regression layer, significantly boosting transfer learning capabilities. We validate the robustness and the practicality of transfer learning across different vehicle classes in a simulation environment, showcasing its broad applicability and effectiveness.

Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focusing on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in CAVs at the macroscopic level. First, this review starts with vehicle key state estimation, specifically vehicle sideslip angle, which is the most pivotal state for vehicle trajectory control, to discuss representative approaches. Then, we present symbolic vehicle trajectory tracking control approaches for AVs. On top of that, we further review the collaborative control frameworks for CAVs and corresponding applications. Finally, this survey concludes with a discussion of future research directions and the challenges. This survey aims to provide a contextualized and in-depth look at the state of the art in vehicle control for AVs and CAVs, identifying critical areas of focus and pointing out the potential areas for further exploration.

Research on game-based human-machine shared driving (HMSD) has traditionally been constrained by stringent game theory assumptions, high requirements for automated steering controllers, and the need to acquire the human driver’s target trajectory. To address these issues, this paper proposes a bargaining game-based HMSD control authority allocation strategy for a steer-by-wire vehicle. First, two types of lateral path tracking controllers are developed to simulate the human driver and the automatic steering system. Then, a bargaining game-based HMSD control authority allocation strategy is created using the Nash bargaining solution. Finally, a Simulink/CARSIM co-simulation platform and a driver-in-the-loop experimental bench are utilized to verify the proposed strategy. The results show that the proposed bargaining game-based HMSD control authority allocation strategy has potential advantages over existing game-based HMSD, including decoupling of human-machine interaction, lower requirements for automated steering controllers, and feasible game modeling ability. Moreover, the proposed strategy demonstrates excellent performance in terms of shared driving, which extends the applicability of HMSD in various operating maneuvers. Both the human driver and the automatic steering system benefit from the proposed HMSD system, which could increase the acceptance and trustworthiness of the human driver with the HMSD system and reduce the human driving load to make the driver-vehicle interaction more reliable and smoother.

Device-to-device (D2D) links scheduling for avoiding excessive interference is critical to the success of wireless D2D communications. Most of the traditional scheduling schemes only consider the maximum throughput or fairness of the system and do not consider the freshness of information. In this paper, we propose a novel D2D links scheduling scheme to minimize the average age of information (AoI) of wireless D2D communications. It is motivated by the fact that the more links are activated, the greater the interference with each other, which reduces the probability of successful transmission and in turn increases the AoI. We thus derive the accurate expression of the overall average AoI of the network based on the transmission success probability under the interfering channels. Moreover, a neural network structure is proposed to learn the mapping from the geographic location to the minimum AoI scheduling under a stationary randomized policy, where the scheduling decision can be made without estimating the channel state information. Finally, numerical results reveal that the performance of the deep learning approach is close to that of a local optimal algorithm which has a higher computational complexity.

Machine-type communication (MTC) is the key technology to support data transfer among devices (sensors and actuators) in Internet of Things (IoT). However, MTC, especially when applied to massive low-power IoT (mIoT), poses some unique and serious challenges due to the low-cost and low-power nature of an mIoT device. One of the most challenging issues is providing a robust way for an mIoT device to acquire the network under a large frequency offset/error (due to the use of a low-cost crystal oscillator) and a low operating SNR (due to the extended coverage). We address the issues in the existing mIoT system acquisition, particularly the initial synchronization waveform detection, and derive a new synchronization waveform that is more robust in an mIoT environment. The mathematical approach provides a useful analytical insight into the design of the synchronization signal waveform for the 5G mIoT system.

Zadoff-Chu (ZC) sequences have been used as synchronization sequences in modern wireless communication systems, replacing the conventional pseudorandom noise sequences due to their perfect autocorrelation properties. We first study the problem of ambiguity between a timing offset and a frequency offset, which arises when a ZC sequence is used as a synchronization signal. We then show how a frequency offset can impair the timing property of a ZC sequence, causing irreducible timing errors. An analytical framework, particularly the timing spectrum, is developed, which fully characterizes a ZC sequence's timing properties and its fundamental limitations as a time synchronization sequence in the presence of a frequency offset between the transmitter and the receiver. This analytical framework provides a powerful analytical tool for timing signal design and performance analysis of ZC sequences.

The FCC new regulation for cognitive use of the TV white space spectrum provides a new means for improving traditional cellular network performance. But it also introduces a number of technical challenges. This paper studies one of the challenges: given the significant differences in the propagation property and the transmit power limitations between the cellular band and the TV white space, how both bands can be jointly utilized such that the benefit from the TV white space is maximized for overall cellular network performance improvement. Both analytical and simulation results are provided.

Orthogonal frequency division multiple access (OFDMA) and Single Carrier Frequency Division Multiple Access (SC-FDMA) are the two major multiple access schemes for 4G wireless communications. In long term evolution (LTE) the downlink multiple access scheme is based on OFDMA and the uplink multiple access scheme is based on SC-FDMA. In this letter, we derive the fundamental performance difference between OFDMA and SC-FDMA, and present the general performance comparison between them. Analytical results show that OFDMA performance upper bounds SC-FDMA in the sense of capacity. The conclusion is further confirmed by numerical results.

Zadoff-Chu (ZC) sequences have been used as random access sequences in modern wireless communication systems, replacing the conventional pseudo-random-noise (PN) sequences due to their superior autocorrelation properties. An analytical framework quantifying the ZC sequence's performance and its fundamental limitation as a random access sequencein the presence of frequency offset between the transmitter and the receiver is introduced. We show that a ZC sequence's perfect autocorrelation properties can be severely impaired by the frequency offset thereby limiting the overall performance of the random access signals formed from these sequences. First, we derive the autocorrelation function of these random access sequences as a function of the frequency offset. Next, we introduce the concept of critical frequency offsets and the spectrum associated with a ZC sequence set to characterize the frequency offset properties of the random access signals. Finally, we demonstrate that the frequency offset immunity of a ZC sequence set can be controlled by shaping the spectrum of the ZC sequence set.