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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.

Abstract This paper investigates the formation control of connected autonomous vehicle (CAV) platoons moving in multi lanes using distance‐based formation control techniques based on rigid graphs and V2V communication. A hierarchical architecture is proposed to decompose the cooperative control into velocity planning and vehicle dynamic control. A new velocity planning method is first developed via a distributed distance‐based formation controller so that each vehicle can keep platoon and change lane. Then, for the vehicle dynamics with nonlinearities and bounded disturbances, an adaptive controller is designed for regulating driving/braking torque to achieve the longitudinal velocity output of the velocity planner. The steering controller is designed to adjust the yaw angle of each vehicle to track and change lanes. Furthermore, stability analysis is conducted based on the Lyapunov theory. Finally, the applications of the proposed control designs to various of automated highway system (AHS) scenarios including lane‐change, curve lane and platoon overtaking, are simulated and numerically analysed to validate the effectiveness of theoretical results.

Coordinated beamforming is very efficient at managing interference in ultra dense network. However, the optimal strategy remains as a challenge task to obtain due to the coupled nature among densely and autonomously deployed cells. In this paper, the deep reinforcement learning is investigated for predicting coordinated beamforming strategy. Formulated as a sum-rate maximization problem, the optimal solution turns out as a balanced combination of selfish and altruistic beamforming. As the balancing coefficients depend on the beamforming vectors of all the cells, iterations are inevitable to get the final solution. To address this problem and improve efficiency, deep reinforcement learning (DL) is proposed to predict the balancing coefficients. Specifically, the agent, on behalf of a base station-user pair, will rely on Deep Q-network to learn the highly complex mapping between the balancing coefficients and signal-interference environment of each user. Subsequently, the beamforming vectors are obtained efficiently through the learned balancing coefficients. Due to the distinguished feature in exploration of the beamforming parameterization, the complexity problem brought by predicting the beamforming matrix directly is avoided. The performance of the proposed scheme is investigated by experiments with arguments regarding multiple input and multiple output configuration, shadow fading and state design. Simulation results indicate the facts that: 1) the theoretically infinite strategy space can be discretized with limited levels and granularity;2)it is feasible to approximate the complex mapping by Q-learning for wireless channel consisting both the large and small scale fading, 3) the balancing coefficients only concerns large scale fading, so the coordinated beamforming can be decomposed to two sub-problems with different time scales: parameterization at large time scales and instant beamforming based on balancing coefficients.

Ultra-Dense Network (UDN) is one of the key techniques for the next generation of mobile network due to providing high system throughput. However, severe interference often occurs in UDN, which greatly impact the data rates of cell-edge users. User-centric wireless access virtualization has been widely adopted in UDN to mitigate the interference of cell-edge users by sharing resources and eliminating cell boundary. However, it's only effective for moderate scale networks. Moreover, the efficiency needs further improvement. In this paper, we study effective cooperative clustering method for large scale UDN with less computations in order to improve the throughput of cell-edge users. We formulate a convex optimization problem in which the objective is to maximize the system throughput with overlapping virtual cells. We propose a clustering method to solve this optimization problem. We design a fast-convergent iterative algorithm called K-Nearest Neighbor (KNN) algorithm to perform users clustering. Simulation results show that our proposed algorithm has better throughput performance for both average and cell-edge users. Especially, the per-carrier throughput is improved, which leads to more serviceable users with limited resources.

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.

The control problem for quadrotor UAVs is difficult and challenging due to the complex nonlinear dynamics and ever-changing disturbances. In this paper, a supplementary controller based on reinforcement learning (RL) is proposed to improve the control performance of quadrotor UAVs. The proposed RL method is constructed by an actor-critic structure and some improved technologies, e.g., Q-learning, temporal difference, and experience replay. With the proposed method, the speed and stability of training can be improved greatly. On one hand, the supplementary controller can work together with the traditional controller online, which can guarantee the stability of the system. On the other hand, the model uncertainties and external disturbances could be restrained through online RL training. The Lyapunov theory is used to prove the convergence of the RL controller's weights theoretically. Finally, three simulations are provided to illustrate the effectiveness of the proposed controller.

In this paper, a nonlinear robust control method is developed for trajectory tracking of a quadrotor aircraft. The proposed approach combines the robust signal compensation method and the backstepping technique. First, the quadrotor dynamic system with multiple disturbances and uncertainties is divided into four subsystems. Then, the nominal controllers are constructed for the subsystems without disturbance terms, while the robust signal compensators are introduced to compensate for the effect of nonlinearities and uncertainties. Furthermore, the Lyapunov analysis has shown that the proposed controller can achieve stable tracking in the presence of violent discontinuous disturbances and uncertainties. The tracking error converges to an arbitrarily small neighborhood of zero. Finally, the proposed control design is validated with real flight experiments. The results show superior trajectory tracking performance of the quadrotor system affected by external disturbances.

In this paper, the H∞ piecewise control problem is discussed for linear parabolic distributed parameter systems (DPSs) with piecewise observation in space. An H∞ performance constraint is provided in this paper to solve the effect caused by the measurement disturbance. First, a static feedback controller is designed for the collocated piecewise observation in space, then an observer-based dynamic feedback controller is designed for the non-collocated piecewise observation in space. By the Lyapunov technique, it has been proved that if the LMI-based sufficient conditions are fulfilled, the resulting closed-loop systems are exponentially stable under a prescribed H∞ performance constraint. Finally, the numerical simulation results of the closed-loop system and the H∞ attenuation level demonstrate the effectiveness of the proposed method.

This paper investigates the problem of fault detection, isolation, and estimation for a networked system with actuator and sensor faults. To deal with the bandwidth constraint, an event-triggered scheduling mechanism is utilized to determine whether the sensor observation shall be transmitted to the fault filter according to the importance of information. In this study, two independent Markovian jump chains are introduced to describe the temporal occurrence of sensor fault and the random switching between the normal condition and the faulty ones of the actuator, respectively. To alleviate the compromise between the model number of fault models and computational complexity in the existing interacting multiple models (IMM) approaches, a novel event-triggered fault detection and diagnosis algorithm is proposed based on the augmented IMM framework, where the fault location to be detected is added into the model set and the fault amplitude to be estimated is augmented into the system state. Finally, a Monte Carlo simulation involving tracking a two-dimension moving target is provided to illustrate the effectiveness and efficiency of the proposed method.

The autonomous underwater vehicle (AUV) is widely used to search for unknown targets in the complex underwater environment. Due to the unpredictability of the underwater environment, this paper combines the traditional frontier exploration method with deep reinforcement learning (DRL) to enable the AUV to explore the unknown underwater environment autonomously. In this paper, a grid map of the search environment is built by the grid method. The designed asynchronous advantage actor-critic (A3C) network structure is used in the traditional frontier exploration method for target search tasks. This network structure enables the AUV to learn from its own experience and generate search strategies for the various unknown environment. At the same time, DRL and dual-stream Q-learning algorithms are used for AUV navigation to further optimize the search path. The simulations and experiments in an unknown underwater environment with different layouts show that the proposed algorithm can accomplish target search tasks with a high success rate, and it can adapt to different environments. In addition, compared to other search methods, the frontier exploration algorithm based on DRL can search a wider environment faster, which results in a higher search efficiency and reduced search time.

In order to improve the edge caching efficiency of the fog radio access network (F-RAN), this paper put forward a distributed deep Q-learning-based content caching scheme based on user preference prediction and content popularity prediction. Given that the constraint that the storage capacity of each device is limited, and the optimization problem is formulated so as to maximize the caching hit rate. Specifically, by taking users' selfishness into consideration, user preference is predicted in an offline manner by applying popular topic models. Then, the online predicted content popularity is achieved by combining the network topology relationship together with the obtained user preference. Finally, with the predicted user preference and content popularity, the deep Q-learning network (DQN)-based content caching algorithm is proposed to achieve the optimal content caching strategy. Moreover, we further present a content update policy with user preference and content popularity prediction, so that the proposed algorithm can handle the variations of contents popularity in a timely manner. Simulation results demonstrate that the proposed scheme achieves better caching hit rate compared with existing algorithms.

This paper presents the research results of detecting gastric polyps with deep learning object detection method in gastroscopic images. Gastric polyps have various sizes. The difficulty of polyp detection is that small polyps are difficult to detect from the background. We propose a feature extraction and fusion module and combine it with the YOLOv3 network to form our network. This method performs better than other methods in the detection of small polyps because it can fuse the semantic information of high-level feature maps with low-level feature maps to help small polyps detection. In this work, we use a dataset of gastric polyps created by ourselves, containing 1433 training images and 508 validation images. We train and validate our network on our dataset. In comparison with other methods of polyps detection, our method has a significant improvement in precision, recall rate, F1, and F2 score. The precision, recall rate, F1 score, and F2 score of our method can achieve 91.6%, 86.2%, 88.8%, and 87.2%.

Time-varying output formation control problems for high-order time-invariant swarm systems are studied with nonlinear uncertainties and directed network topology in this paper. A robust controller which consists of a nominal controller and a robust compensator is applied to achieve formation control. The nominal controller based on the output feedback is designed to achieve desired time-varying formation properties for the nominal system. And the robust compensator based on the robust signal compensator technology is constructed to restrain nonlinear uncertainties. The time-varying formation problem is transformed into the stability problem. And the formation errors can be arbitrarily small with expected convergence rate. Numerical examples are provided to illustrate the effectiveness of the proposed strategy.

Learning from imbalanced data is a challenging task in the fields of machine learning and data mining. As an effective and efficient solution, cost-sensitive learning has been widely adopted to address class imbalance learning (CIL) problems. Weighted extreme learning machine (WELM), which is constructed based on ELM, is a significant member in the cost-sensitive-learning algorithmic family. WELM can effectively deal with CIL problems. However, it has two main drawbacks: 1) it has high time complexity on large-scale data since a large-matrix multiplication operation is required in the solution procedure and 2) it lacks flexibility since it can only tune the training error for each instance and not for each class label. In this paper, we present an alternative to WELM, which is called label-WELM (LW-ELM). Unlike WELM, LW-ELM copes with CIL problems by tuning the training error of each class label. Specifically, the expected output (or training class label) that corresponds to the minority class is augmented, thereby providing stronger tolerance to training errors of the minority-class instances. In this paper, we design two types of weight allocation strategies, both of which are based on the class-imbalance ratio (CIR). In contrast with WELM, LW-ELM is fast and flexible, where fast means that it has low-time complexity and flexible indicates that it can also be used to tackle imbalanced multi-label learning problems, while WELM cannot. The experimental results on binary-class, multiclass, and multi-label data sets with skewed class distributions show the effectiveness and superiority of the proposed LW-ELM algorithm.

The question of how to estimate the state of an unmanned aerial vehicle (UAV) in real time in multi-environments remains a challenge. Although the global navigation satellite system (GNSS) has been widely applied, drones cannot perform position estimation when a GNSS signal is not available or the GNSS is disturbed. In this paper, the problem of state estimation in multi-environments is solved by employing an Extended Kalman Filter (EKF) algorithm to fuse the data from multiple heterogeneous sensors (MHS), including an inertial measurement unit (IMU), a magnetometer, a barometer, a GNSS receiver, an optical flow sensor (OFS), Light Detection and Ranging (LiDAR), and an RGB-D camera. Finally, the robustness and effectiveness of the multi-sensor data fusion system based on the EKF algorithm are verified by field flights in unstructured, indoor, outdoor, and indoor and outdoor transition scenarios.

The codebook-based beamforming for millimeter-wave (mm Wave) communication systems is usually used to compensate the severe attenuation of the mm Wave region. The beam training process based on pre-specified beam codebooks is considered a global optimization problem in 2-D planes formed by the potential beam index. The Rosenbrock algorithm (RA) is adopted to implement optimum beam searching whereas the simulated annealing (SA) algorithm is used to solve the problem of falling into the local optimum, due to the unavailable gradient information of the objective function. However, the RA implements rounding to the integer which leads to the problem of repeated search and beam space discontinuity caused by beam index will impair the powerful local search ability. Thus, in this paper, an enhanced RA based on tabu search and combined with SA algorithm is proposed as an alternative solution for beam search success rate. The proposed algorithm reduces the search times by forbidding the repeat search with tabu table and design of neighbor region. Moreover, to prevent the search failure, the search candidate index is defined to keep the local search ability of the original algorithm and wrap around of beam index is applied to maintain continuity of the search direction. Experimental simulations show that the proposed technique can improve the search efficiency in terms of reduced steps and increase search success rate during the beam training procedure compared to existing techniques.

In this paper, we investigate the optimal beamforming design to achieve joint congestion control and energy-efficient resource allocation in cache-enabled sensor networks. The network of interest works in the time-slotted mode. The dynamic buffering queue for each node is introduced to reflect the degree of network congestion and service delay. Then, a time-averaged sum rate maximization problem is proposed under the constraints of queue stability, instantaneous power consumption, average power consumption, and the minimum quality of service requirements. By introducing the method of Lyapunov optimization, the importance of buffering queue backlogs and sum rate maximization can be traded off, then the original queue-aware and time-averaged optimization problem is transformed into a weighted sum rate maximization problem at each time slot. It can be further converted into a second-order cone-programming problem by successive convex approximation, which is convex and can be efficiently solved by off-the-shelf solvers. Numerical results validate that wireless caching can greatly relieve the network congestion by reducing the buffering backlogs, and show that the proposed scheme can trade off the average queue length and time-averaged sum rate by selecting different control parameters.

It is well-known that the effects of spatial selection on the two-strategy competition can be quantified by the structural coefficient σ under weak selection. We here calculate the accurate value of σ in group-structured populations of any finite size. In previous similar models, the large population size has been explicitly required for obtaining σ, and here we analyze quantitatively how large the population should be. Unlike previous models which have only involved the influences of the longest and the shortest migration rang on σ, we consider all migration ranges together. The new phenomena are that an intermediate range maximizes σ for medium migration probabilities which are of the tiny minority and the maximum value is slightly larger than those for other ranges. Furthermore, we find the ways that migration or mutation changes σ can vary significantly through determining analytically how the high-frequency steady states (distributions of either strategy over all groups) impact the expression of σ obtained before. Our findings can be directly used to resolve the dilemma of cooperation and provide a more intuitive understanding of spatial selection.