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Recent years have witnessed numerous technical breakthroughs in connected and autonomous vehicles (CAVs). On the one hand, these breakthroughs have significantly advanced the development of intelligent transportation systems (ITSs); on the other hand, these new traffic participants introduce more complex and uncertain elements to ITSs from the social space. Digital twins (DTs) provide real-time, data-driven, precise modeling for constructing the digital mapping of physical-world ITSs. Meanwhile, the metaverse integrates emerging technologies such as virtual reality/mixed reality, artificial intelligence, and DTs to model and explore how to realize improved sustainability, increased efficiency, and enhanced safety. More recently, as a leading effort toward general artificial intelligence, the concept of foundation model was proposed and has achieved significant success, showing great potential to lay the cornerstone for diverse artificial intelligence applications across different domains. In this article, we explore the big models embodied foundation intelligence for parallel driving in cyber-physical-social spaces, which integrate metaverse and DTs to construct a parallel training space for CAVs, and present a comprehensive elucidation of the crucial characteristics and operational mechanisms. Beyond providing the infrastructure and foundation intelligence of big models for parallel driving, this article also discusses future trends and potential research directions, and the “6S” goals of parallel driving.

Abstract The control of unmanned aerial vehicle quadrotor is challenging because of non‐linearities, coupling and disturbance. Here, a novel control method which includes reinforcement learning (RL) component and robust component is proposed. In this method, the RL component only relies on collected data instead of modelling to handle coupling and disturbance from aerodynamics and model. To ensure safety during training and improve training speed, the robust component is used to reduce the disturbance .The stability of the system with our controller is proven by Lyapunov method. The results of the simulation exhibit advanced performance of our controller.

Abstract Gastric cancer is a deadly disease and gastric polyps are at high risk of becoming cancerous. Therefore, the timely detection of gastric polyp is of great importance which can reduce the incidence of gastric cancer effectively. At present, the object detection method based on deep learning is widely used in medical images. However, as the contrast between the background and the polyps is not strong in gastroscopic image, it is difficult to distinguish various sizes of polyps from the background. In this paper, to improve the detection performance metrics of endoscopic gastric polyps, we propose an improved attentional feature fusion module. First, in order to enhance the contrast between the background and the polyps, we propose an attention module that enables the network to make full use of the target location information, it can suppress the interference of the background information and highlight the effective features. Therefore, on the basis of accurate positioning, it can focus on detecting whether the current location is the gastric polyp or background. Then, it is combined with our feature fusion module to form a new attentional feature fusion model that can mitigate the effects caused by semantic differences in the processing of feature fusion, using multi-scale fusion information to obtain more accurate attention weights and improve the detection performance of polyps of different sizes. In this work, we conduct experiments on our own dataset of gastric polyps. Experimental results show that the proposed attentional feature fusion module is better than the common feature fusion module and can improve the situation where polyps are missed or misdetected.

This paper addressed the optimal policy selection problem of attacker and sensor in cyber-physical systems (CPSs) under denial of service (DoS) attacks. Since the sensor and the attacker have opposite goals, a two-player zero-sum game is introduced to describe the game between the sensor and the attacker, and the Nash equilibrium strategies are studied to obtain the optimal actions. In order to effectively evaluate and quantify the gains, a reinforcement learning algorithm is proposed to dynamically adjust the corresponding strategies. Furthermore, security state estimation is introduced to evaluate the impact of offensive and defensive strategies on CPSs. In the algorithm, the ε-greedy policy is improved to make optimal choices based on sufficient learning, achieving a balance of exploration and exploitation. It is worth noting that the channel reliability factor is considered in order to study CPSs with multiple reasons for packet loss. The reinforcement learning algorithm is designed in two scenarios: reliable channel (that is, the reason for packet loss is only DoS attacks) and unreliable channel (the reason for packet loss is not entirely from DoS attacks). The simulation results of the two scenarios show that the proposed reinforcement learning algorithm can quickly converge to the Nash equilibrium policies of both sides, proving the availability and effectiveness of the algorithm.