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A novel energy-saving driving assistance system (DAS) for electric vehicles (EVs) integrated with predictive cruise control (PCC) is proposed in this paper to extend the driving range while guaranteeing travelling safety. Firstly, the energy-efficient driving task is formulated as the optimal control problem (OCP) with multiple constraints under the model predictive control (MPC) scheme and is solved by using Pontryagin's maximum principle. Then, considering the difference between the optimal and driver's action, and the real-time implementation of the control strategy, a novel energy-saving DAS is proposed, in which a quadratic form OCP is further formulated to find the optimal torque distribution. Furthermore, to achieve a better interaction between the system and the driver, a human machine interface (HMI) hardware is developed to provide drivers with visual and auditory prompts, meanwhile, the driver's demanding torque is adjusted dynamically for eco-driving. Finally, the energy-saving DAS is deployed on an EV, and on-road experiments demonstrate that the proposed system can help save an average of 5.19% energy compared to normal driving by improving the efficiency of the driving motor and the regenerative braking energy (RBE).

The urban transportation network is crucial for societal development, but it is prone to failures like congestion caused by accidents or disasters. In particular, often network-wide failure is the result of a series of cascading failures originating from a small set of individual links. To prevent such failures, it is essential to identify these critical links and take early action. However, most existing approaches in the literature for evaluating the importance of each link rely on manually designed metrics (e.g., the Network Robustness Index). These methods are time-consuming and not suitable for large-scale urban networks. Additionally, these metrics fail to accurately capture the dynamic traffic interactions influenced by vehicle movement. In this paper, we present a novel method for identifying critical links by learning effective traffic interaction representation (the spatio-temporal dependencies) among roads. By representing the network as an un-directed graph and abstracting the road links as the nodes, we introduce a temporal graph attention model to capture spatial and temporal dependence between nodes. This model combines a graph attention network and a long short-term memory neural network and produces an attention matrix, which represents traffic interactions among links. Furthermore, we propose a traffic influence propagation model to evaluate the influence of each link for the entire road network based on the traffic interaction representation. We rank the importance of links based on their influence and then identify the critical links. A real-world case study in the city of Hangzhou, China is conducted to test our method and we use the network efficiency ratio to quantify its performance. The results suggest that our method can effectively identify the critical links at different periods.

Advanced Air Mobility (AAM) envisages a sustainable, safe, convenient, and affordable air transport system. In socio-technical transition of AAM, there are a number of trade-offs in ecosystem that need to be studied. Three perspectives on economic feasibility are explored: first, based on history of VTOL services and value of time estimates, we discuss whether AAM can provide customers with competitive mobility services; second, what are the stakeholders’ insights on the deployment of AAM; last, the experience in the development of autonomous driving technology, such as parallel intelligence, can inform future AAM research.

Automation has come a long way since the early days of mechanization, i.e., the process of working exclusively by hand or using animals to work with machinery. The rise of steam engines and water wheels represented the first generation of industry, which is now called Industry 1.0. Subsequently, Industry 2.0 witnessed the development of electric power and assembly lines. Later on, programmable logic controllers and Human Machine Interfaces (HMI) were the new productivity tools in Industry 3.0, which enabled precise and consistent production. In recent years, Industry 4.0 absorbed the latest technologies of Internet of Things (IoT), Artificial Intelligence (AI), and big data, making production processes integrated, interconnected, and smart. Nowadays, Industry 5.0 has been proposed, which emphasizes human-centric automation. Specifically, the new concept of automation in Industry 5.0, named Automation 5.0, is no longer about how to create machinery to replace humans. Instead, it aims to reach organic interactions and cooperation between humans and machines, meeting the goal of “6S” - Safety, Security, Sustainability, Sensitivity, Service, and Smartness [1]–[4] - and the overall objective of deploying automation for the better, human-friendly, and smarter industry.

Interactive decision-making between multiple automated vehicles under unsigned intersections is a high-level dynamic decision-making scenario, greatly increasing the complexity of decision-making. In this situation, making the decision-making manner in accordance with the logic of human and guaranteeing driving safety is technically challenging. A multi-factor-enabled interactive decision-making method is proposed in this paper to realize such behavior, which employs multiple complementary factors and switchable modes in a dynamic game. More specifically, these factors are driving performance requirements, e.g., moving safety, smoothness comfort, fast passing, and surrounding space, as well as diversified driving styles suitable for different driver groups. Meanwhile, to improve the reasonability of automated driving and reduce the complexity of multi-vehicle games, switchable game modes are established to realize the dynamic adjustment mechanism. The effectiveness of the proposed method in resolving conflicts in a continuous interactive way is verified through extensive simulations. The results indicate the proposed method can reflect the interaction process between multi-agents, and improve compliance between intelligent decision-making and the logic of human.

Very recently, intensive discussions and studies on Industry 5.0 have sprung up and caused the attention of researchers, entrepreneurs, and policymakers from various sectors around the world. However, there is no consensus on why and what is Industry 5.0 yet. In this paper, we define Industry 5.0 from its philosophical and historical origin and evolution, emphasize its new thinking on virtual-real duality and human-machine interaction, and introduce its new theory and technology based on parallel intelligence (PI), artificial societies, computational experiments, and parallel execution (the ACP method), and cyber-physical-social systems (CPSS). Case studies and applications of Industry 5.0 over the last decade have been briefly summarized and analyzed with suggestions for its future development. We believe that Industry 5.0 of virtual-real interactive parallel industries has great potentials and is critical for building smart societies. Steps are outlined to ensure a roadmap that would lead to a smooth transition from CPS-based Industry 4.0 to CPSS-based Industry 5.0 for a better world which is Safe in physical spaces, Secure in cyberspaces, Sustainable in ecology, Sensitive in indi-vidual privacy and rights, Service for all, and Smartness of all.