When terrestrial communication base stations are destroyed, communication efficiency and emergency response capability are severely degraded. To address this challenge, we propose a rapid UAV enabled communication service within a low altitude intelligent networking architecture. The method adopts the Soft Actor Critic reinforcement learning framework and integrates a Transformer as the spatial structure feature extractor to enable intelligent UAV deployment in complex post disaster environments. Experiments on real geographic coordinate data compare Graph Convolutional Network and Graph Attention Network feature extractors with respect to coverage rate, normalized delay, and energy cost. The SAC-Transformer achieves an average coverage of 99.8%, improving over SAC-GAT, SAC, and SAC-GCN by 5.83%, 14.45%, and 15.64%, respectively. Its average delay is reduced by 39.43%, 47.80%, and 32.56% relative to SAC-GAT, SAC, and SAC-GCN, respectively. Overall, the SAC-Transformer markedly enhances coverage and robustness in complex post disaster scenarios and exhibits stronger multi objective performance with high coverage, low delay, and controllable energy cost, making it well suited for UAV emergency deployments in spatially complex settings with stringent communication constraints.

Monocular 3D lane detection plays a crucial role in autonomous driving, assisting vehicles in safe navigation. Existing methods primarily utilize calibrated camera parameters in the dataset to conduct 3D lane detection from a single image. However, errors or sudden absence of camera parameters can pose significant challenges to safe driving. On one hand, this can lead to incorrect feature acquisition, which further affects the precision of lane detection. On the other hand, it renders methods relying on transformation matrices for temporal fusion ineffective. To address the above issue and achieve accurate 3D lane detection, we propose CaliFree3DLane, a calibration-free method for spatio-temporal 3D lane detection based on Transformer structure. Instead of using geometric projections to obtain static reference points on images, we propose a reference point refinement strategy that dynamically updates the reference points and finally generates appropriate sampling points for image feature extraction. To integrate multi-frame features, we generate sub-queries from the current scene query to focus on the image features of each frame independently. We then aggregate these sub-queries to form a more comprehensive scene query for 3D lane detection. Using these operations, CaliFree3DLane accurately transforms multi-frame image features into the current bird’s-eye view (BEV) space, enabling precise 3D lane detection. Experimental results show that our CaliFree3DLane achieves state-of-the-art 3D lane detection performance in various datasets. Compared to the Transformer-based methods of the same type, we have also improved$ {6.0\%}\sim {10.5\%}$at the F1 score. Code is available athttps://github.com/Ciisrlab/CaliFree3DLane.

Abstract It is a challenging task to find a feasible path from the start to the end for heterogeneous group and avoid collision between dynamic agents and static obstacles. The existing methods are usually applicable to static simple scenes or the type of scenes including a single kind of agents, and it is difficult to meet the high dynamic heterogeneous group movements. To address the above issues, we propose a hybrid driven heterogeneous group path planning method based on data and mechanism model. Data and mechanism model are combined to drive movements of heterogeneous groups. The experimental results show that our method can describe movements of heterogeneous groups more realistically and solve the collision avoidance of heterogeneous groups well. We quantitatively evaluate our method using metrics such as the number of inflection points and the average turning angle. Average turning angle has decreased by 59.50% on average over prior methods. Number of inflection points has decreased by 69.19% on average over prior methods.

Predicting accurate future trajectories of agents is essential for autonomous navigation in complex scenarios. Although numerous work has made great progress on this goal, it is still challenging due to the uncertainty and continuity of behavioral intentions of agents, where uncertainty means the instantaneous multimodality of motion behavior, while the continuity refers to the consistency and stability of behavioral intention of an agent over a period of time constrained by its final destination. These factors easily affect the improvement of prediction accuracy. In this paper, we present a novel trajectory prediction method, Stacked Conditional VAE (S-CVAE) with Incremental Greedy Region (IGR). Specifically, the IGR is designed to enlarge the coverage of candidate waypoints/endpoints by reformulating the waypoints/endpoints prediction problem as candidate region generation, which can further encourage and model multimodality of behavioral intentions. Meanwhile, to exploit the inherent continuity between adjacent behavioral intentions of an agent, the S-CVAE architecture is constructed to transmit the behavioral intentions of one agent by inserting intermediate waypoints with IGR into the potential trajectories from the observed path to the final endpoint, and also enhances the reliability of the generated waypoints/endpoints in the next moment, further improve the accuracy of trajectory prediction. Our method is evaluated on several public datasets, including nuScenes, Apolloscape, SDD, INTERSECTION, Waymo, and VTPTL. The comprehensive experimental results demonstrate that our method achieves significant performance on these datasets. Especially in nuScenes and VTPTL, the accuracy is increased by at least 11.11% on average ADE and 2.40% on average FDE compared with state-of-the-arts.

Traffic intersections are important scenes that can be seen almost everywhere in the traffic system. Currently, most simulation methods perform well at highways and urban traffic networks. In intersection scenarios, the challenge lies in the lack of clearly defined lanes, where agents with various motion plannings converge in the central area from different directions. Traditional model-based methods are difficult to drive agents to move realistically at intersections without enough predefined lanes, while data-driven methods often require a large amount of high-quality input data. Simultaneously, tedious parameter tuning is inevitable involved to obtain the desired simulation results. In this paper, we present a novel adaptive and planning-aware hybrid-driven method (TraInterSim) to simulate traffic intersection scenarios. Our hybrid-driven method combines an optimization-based data-driven scheme with a velocity continuity model. It guides the agent's movements using real-world data and can generate those behaviors not present in the input data. Our optimization method fully considers velocity continuity, desired speed, direction guidance, and planning-aware collision avoidance. Agents can perceive others' motion plannings and relative distances to avoid possible collisions. To preserve the individual flexibility of different agents, the parameters in our method are automatically adjusted during the simulation. TraInterSim can generate realistic behaviors of heterogeneous agents in different traffic intersection scenarios in interactive rates. Through extensive experiments as well as user studies, we validate the effectiveness and rationality of the proposed simulation method.

The antagonistic behavior in the crowd usually exacerbates the seriousness of the situation in sudden riots, where the antagonistic emotional contagion and behavioral decision making play very important roles. However, the complex mechanism of antagonistic emotion influencing decision making, especially in the environment of sudden confrontation, has not yet been explored very clearly. In this paper, we propose an Emotional contagion-aware Deep reinforcement learning model for Antagonistic Crowd Simulation (ACSED). First, we build a group emotional contagion module based on the improved Susceptible Infected Susceptible (SIS) infection disease model, and estimate the emotional state of the group at each time step during the simulation. Then, the tendency of crowd antagonistic action is estimated based on Deep Q Network (DQN), where the agent learns the action autonomously, and leverages the mean field theory to quickly calculate the influence of other surrounding individuals on the central one. Finally, the rationality of the predicted actions by DQN is further analyzed in combination with group emotion, and the final action of the agent is determined. The proposed method in this paper is verified through several experiments with different settings. We can conclude antagonistic emotions play a critical role in the decision making of the crowd through influencing the individual behavior in the riot scenario, where individual behaviors are primarily driven by emotions and goals, rather than common rules. The experiment results also prove that the antagonistic emotion has a vital impact on the group combat, and positive emotional states are more conducive to combat. Moreover, by comparing the simulation results with real scenes, the feasibility of our method is further confirmed, which can provide good reference to formulate battle plans and improve the win rate of righteous groups in a variety of situations.

Aiming at the topology of high-frequency link dual matrix inverter (HFLDMI), an intermediate hexagonal modulation strategy based on topology decoupling and vector decoupling is proposed, which effectively suppresses the common mode voltage (CMV) of the system and realizes soft switching of switches. Firstly, the working mode of HFLDMI under the proposed modulation strategy is analyzed in detail. After that, the accurate switching models of CMV and zero sequence voltage (ZSV) of HFLDMI are established and the CMV and ZSV of HFLDMI are compared with the CMV and ZSV of a conventional dual inverter. Finally, the correctness of the proposed modulation strategy is verified by simulation software. The results show that the CMV amplitude of HFLDMI is suppressed below V dc /6 and the ZSV is zero.

To meet the charging requirements of constant current output (CCO) and constant voltage output (CVO), and maintain constant output when the couplers are misaligned in inductive power transfer (IPT) system, a hybrid three-coil IPT topology with high misalignment tolerance is presented. Two mode conversion switches, one inductor and two capacitors are added on the receiver side of the conventional series series (SS) topology to realize the switching SS and S/LCC topologies, so as to achieve the CCO and CVO. The system combines all the advantages of the two topologies, does not need to switch frequency, and has simple control. Moreover, it gets rid of the wireless communication between the receiver and the transmitter. The transmitter adopts two coils connected in reverse series to improve system’s misalignment tolerance. The operating principle of the topology is introduced in detail, and the main idea of parameter design is also given. Ultimately, a 100W experimental prototype is built to verify the effectiveness and superiority of the proposed hybrid IPT topology.

Antagonistic crowd behaviors are often observed in cases of serious conflict. Antagonistic emotions, which is the typical psychological state of agents in different roles (i.e., cops, activists, and civilians) in crowd violence scenes, and the way they spread through contagion in a crowd are important causes of crowd antagonistic behaviors. Moreover, games, which refers to the interaction between opposing groups adopting different strategies to obtain higher benefits and less casualties, determine the level of crowd violence. We present an antagonistic crowd simulation model (ACSEE), which is integrated with antagonistic emotional contagion and evolutionary game theories. Our approach models the antagonistic emotions between agents in different roles using two components: mental emotion and external emotion. We combine enhanced susceptible-infectious-susceptible (SIS) and game approaches to evaluate the role of antagonistic emotional contagion in crowd violence. Our evolutionary game theoretic approach incorporates antagonistic emotional contagion through deterrent force, which is modelled by a mixture of emotional forces and physical forces defeating the opponents. Antagonistic emotional contagion and evolutionary game theories influence each other to determine antagonistic crowd behaviors. We evaluate our approach on real-world scenarios consisting of different kinds of agents. We also compare the simulated crowd behaviors with real-world crowd videos and use our approach to predict the trends of crowd movements in violence incidents. We investigate the impact of various factors (number of agents, emotion, strategy, etc.) on the outcome of crowd violence. We present results from user studies suggesting that our model can simulate antagonistic crowd behaviors similar to those seen in real-world scenarios.

Corona Virus Disease 2019 (COVID-19), due to its extremely high infectivity, has been spreading rapidly around the world and bringing huge influence to socioeconomic development and people’s daily life. Taking for example the virus transmission that may occur after college students return to school, we analyze the quantitative influence of the key factors on the virus spread, including crowd density and self-protection. One Campus Virus Infection and Control Simulation (CVICS) model of the novel coronavirus is proposed in this article, fully considering the characteristics of repeated contact and strong mobility of crowd in the closed environment. Specifically, we build an agent-based infection model, introduce the mean field theory to calculate the probability of virus transmission, and microsimulate the daily prevalence of infection among individuals. The experimental results show that the proposed model in this article efficiently simulates how the virus spreads in the dense crowd in frequent contact under a closed environment. Furthermore, preventive and control measures, such as self-protection, crowd decentralization, and isolation during the epidemic, can effectively delay the arrival of infection peak, reduce the prevalence, and, finally, lower the risk of COVID-19 transmission after the students return to school.

Increasing attention is being given to the modeling and simulation of traffic flow and crowd movement, two phenomena that both deal with interactions between pedestrians and cars in many situations. In particular, crowd simulation is important for understanding mobility and transportation patterns. In this paper, we propose an emotion-based crowd simulation model integrating physical strength consumption. Inspired by the theory of “the devoted actor,” the movements of each individual in our model are determined by modeling the influence of physical strength consumption and the emotion of panic. In particular, human physical strength consumption is computed using a physics-based numerical method. Inspired by the James-Lange theory, panic levels are estimated by means of an enhanced emotional contagion model that leverages the inherent relationship between physical strength consumption and panic. To the best of our knowledge, our model is the first method integrating physical strength consumption into an emotion-based crowd simulation model by exploiting the relationship between physical strength consumption and emotion. We highlight the performance on different scenarios and compare the resulting behaviors with real-world video sequences. Our approach can reliably predict changes in physical strength consumption and panic levels of individuals in an emergency situation.

Numerous research efforts have been conducted to simulate the crowd movements, while relatively few of them are specifically focused on multihazard situations. In this paper, we propose a novel crowd simulation method by modeling the generation and contagion of panic emotion under multihazard circumstances. In order to depict the effect from hazards and other agents to crowd movement, we first classify hazards into different types (transient and persistent, concurrent and nonconcurrent, and static and dynamic) based on their inherent characteristics. Second, we introduce the concept of perilous field for each hazard and further transform the critical level of the field to its invoked-panic emotion. After that, we propose an emotional contagion model to simulate the evolving process of panic emotion caused by multiple hazards. Finally, we introduce an emotional reciprocal velocity obstacles (RVOs) model to simulate the crowd behaviors by augmenting the traditional RVO model with emotional contagion, which for the first time combines the emotional impact and local avoidance together. Our experimental results demonstrate that the overall approach is robust, can better generate realistic crowds and the panic emotion dynamics in a crowd. Furthermore, it is recommended that our method can be applied to various complex multihazard environments.

In this paper, we present a novel crowd behavior evolution method with emotional contagion in political rallies. We first analyze the most representative political rally scenes in detail and model them into two kinds of abstract scenario. Furthermore, the “extroversion” and “empathy” factors from the OCEAN model are chosen to describe the most important individual personalities in such scenarios. Based on this, an improved emotional contagion model is proposed by combining the Susceptible-Infected-Recovered model and individual personality under different political viewpoints. Finally, the crowd in a political rally is driven to move according to the new potential moving direction generated by emotional contagion and the original direction of the individual together. The experiments show that our method can intuitively demonstrate the emotional changes of those individuals with different political perspectives and reasonably simulate the crowd movement under the political rally scenes.

This paper is interested in the receding horizon estimation problem for the networked control systems with packet losses. Different from the scalar data losses case, we introduce a diagonal matrix to represent the phenomenon of packet losses, where each element of the diagonal matrix is a binary stochastic variable and indicates the arrival of the corresponding measurement component. That means different observation components have different packet loss probability. A batch form and a recursive form for the receding horizon estimation are proposed by minimizing a new cost function that includes two terminal weighting terms. Using the derived condition, the stability of the proposed receding horizon estimation is proved. Finally, a numerical example is provided for illustration.