At present,albeit the dexterous hand prostheses of multiple degrees of freedom(DOFs)have become prosperous on the market,the user’s demand on intuitively operating these devices have not been well addressed so that t...At present,albeit the dexterous hand prostheses of multiple degrees of freedom(DOFs)have become prosperous on the market,the user’s demand on intuitively operating these devices have not been well addressed so that their acceptance rate is relatively low.The unintuitive control method and inadequate sensory feedback are frequently cited as the two barriers to the successful application of these dexterous products.Recently,driven by the wave of artificial intelligence(AI),a series of shared control methods have emerged,in which"bodily function"(myoelectric control)and"artificial intelligence"(local autonomy,computer vision,etc.)are tightly integrated,and provided a new conceptual solution for the intuitive operation of dexterous prostheses.In this paper,the background and development trends of this type of methods are described in detail,and the potential development directions and the key technologies that need breakthroughs are indicated.In practice,we instantiate this shared control strategy by proposing a new method combining simultaneous myoelectric control,multi-finger grasp autonomy,and augmented reality(AR)feedback together.This method"divides"the human sophisticated reach-and-grasp task into several subtasks,and then"conquers"them by using different strategies from either human or machine perspective.It is highly expected that the shared control methods with hybrid human-machine intelligence could address the control problem of dexterous prostheses.展开更多
This paper presents an optimized shared control algorithm for human–AI interaction, implemented through a digital twin framework where the physical system and human operator act as the real agent while an AI-driven d...This paper presents an optimized shared control algorithm for human–AI interaction, implemented through a digital twin framework where the physical system and human operator act as the real agent while an AI-driven digital system functions as the virtual agent. In this digital twin architecture, the real agent acquires an optimal control strategy through observed actions, while the AI virtual agent mirrors the real agent to establish a digital replica system and corresponding control policy. Both the real and virtual optimal controllers are approximated using reinforcement learning(RL) techniques. Specifically, critic neural networks(NNs) are employed to learn the virtual and real optimal value functions, while actor NNs are trained to derive their respective optimal controllers. A novel shared mechanism is introduced to integrate both virtual and real value functions into a unified learning framework, yielding an optimal shared controller. This controller adaptively adjusts the confidence ratio between virtual and real agents, enhancing the system's efficiency and flexibility in handling complex control tasks. The stability of the closed-loop system is rigorously analyzed using the Lyapunov method. The effectiveness of the proposed AI–human interactive system is validated through two numerical examples: a representative nonlinear system and an unmanned aerial vehicle(UAV) control system.展开更多
Shared control schemes allow a human driver to work with an automated driving agent in driver-vehicle systems while retaining the driver’s abilities to control.The human driver,as an essential agent in the driver-veh...Shared control schemes allow a human driver to work with an automated driving agent in driver-vehicle systems while retaining the driver’s abilities to control.The human driver,as an essential agent in the driver-vehicle shared control systems,should be precisely modeled regarding their cognitive processes,control strategies,and decision-making processes.The interactive strategy design between drivers and automated driving agents brings an excellent challenge for human-centric driver assistance systems due to the inherent characteristics of humans.Many open-ended questions arise,such as what proper role of human drivers should act in a shared control scheme?How to make an intelligent decision capable of balancing the benefits of agents in shared control systems?Due to the advent of these attentions and questions,it is desirable to present a survey on the decision making between human drivers and highly automated vehicles,to understand their architectures,human driver modeling,and interaction strategies under the driver-vehicle shared schemes.Finally,we give a further discussion on the key future challenges and opportunities.They are likely to shape new potential research directions.展开更多
A shared control of highly automated Steer-by-Wire system is proposed for cooperative driving between the driver and vehicle in the face of driver's abnormal driving. A fault detection scheme is designed to detect...A shared control of highly automated Steer-by-Wire system is proposed for cooperative driving between the driver and vehicle in the face of driver's abnormal driving. A fault detection scheme is designed to detect the abnormal driving behaviour and transfer the control of the car to the automatic system designed based on a fault tolerant model predictive control(MPC) controller driving the vehicle along an optimal safe path.The proposed concept and control algorithm are tested in a number of scenarios representing intersection, lane change and different types of driver's abnormal behaviour. The simulation results show the feasibility and effectiveness of the proposed method.展开更多
Due to the critical defects of techniques in fully autonomous vehicles,man-machine cooperative driving is still of great significance in today’s transportation system.Unlike the previous shared control structure,this...Due to the critical defects of techniques in fully autonomous vehicles,man-machine cooperative driving is still of great significance in today’s transportation system.Unlike the previous shared control structure,this paper introduces a double loop structure which is applied to indirect shared steering control between driver and automation.In contrast to the tandem indirect shared control,the parallel indirect shared control put the authority allocation system of steering angle into the framework to allocate the corresponding weighting coefficients reasonably and output the final desired steering angle according to the current deviation of vehicle and the accuracy of steering angles.Besides,the active disturbance rejection controller(ADRC)is also added in the frame in order to track the desired steering angle fleetly and accurately as well as restrain the internal and external disturbances effectively which including the steering friction torque,wind speed and ground interference etc.Eventually,we validated the advantages of double loop framework through three sets of double lane change and slalom experiments,respectively.Exactly as we expected,the simulation results show that the double loop structure can effectively reduce the lateral displacement error caused by the driver or the controller,significantly improve the tracking precision and keep great performance in trajectory tracking characteristics when driving errors occur in one of driver and controller.展开更多
The driver-automation shared driving is a transition to fully-autonomous driving,in which human driver and vehicular controller cooperatively share the control authority.This paper investigates the shared steering con...The driver-automation shared driving is a transition to fully-autonomous driving,in which human driver and vehicular controller cooperatively share the control authority.This paper investigates the shared steering control of semi-autonomous vehicles with uncertainty from imprecise parameter.By considering driver’s lane-keeping behavior on the vehicle system,a driver-automation shared driving model is introduced for control purpose.Based on the interval type-2(IT2)fuzzy theory,moreover,the driver-automation shared driving model with uncertainty from imprecise parameter is described using an IT2 fuzzy model.After that,the corresponding IT2 fuzzy controller is designed and a direct Lyapunov method is applied to analyze the system stability.In this work,sufficient design conditions in terms of linear matrix inequalities are derived,to guarantee the closed-loop stability of the driver-automation shared control system.In addition,an H∞performance is studied to ensure the robustness of control system.Finally,simulation-based results are provided to demonstrate the performance of proposed control method.Furthermore,an existing type-1 fuzzy controller is introduced as comparison to verify the superiority of the proposed IT2 fuzzy controller.展开更多
Contemporary autonomous-driving technology relies on good environmental-perception systems and high-precision maps.For unknown environments or scenarios where perception fails,a human-in-the-loop remote-driving system...Contemporary autonomous-driving technology relies on good environmental-perception systems and high-precision maps.For unknown environments or scenarios where perception fails,a human-in-the-loop remote-driving system can effectively complement common solutions,although safety remains an issue for its application.A haptic shared-control algorithm based on non-cooperative game theory is presented in this paper.The algorithm generates collision-free reference paths with model predictive control and predicts the driver’s path using a two-point preview model.Man-machine torque interaction is modeled as a Nash game,and the assist system’s degree of intervention is regulated in real time,according to assessments of collision risk and the driver’s concentration.Simulations of several representative scenarios demonstrate how the proposed method improves driving safety,while respecting driver decisions.展开更多
This paper presents a Shared Control Architecture(SCA)between a human pilot and a smart inceptor for nonlinear Pilot Induced Oscillations(PIOs),e.g.,category II or III PIOs.One innovation of this paper is that an inte...This paper presents a Shared Control Architecture(SCA)between a human pilot and a smart inceptor for nonlinear Pilot Induced Oscillations(PIOs),e.g.,category II or III PIOs.One innovation of this paper is that an intelligent shared control architecture is developed based on the intelligent active inceptor technique,i.e.,Smart Adaptive Flight Effective Cue(SAFE-Cue).A deep reinforcement learning approach namely Deep Deterministic Policy Gradient(DDPG)method is chosen to design a gain adaptation mechanism for the SAFE-Cue module.By doing this,the gains of the SAFE-Cue will be intelligently tuned once nonlinear PIOs triggered;meanwhile,the human pilot will receive a force cue from the SAFE-Cue,and will consequently adapting his/her control policy.The second innovation of this paper is that the reward function of the DDPG based gain adaptation approach is constructed according to flying qualities.Under the premise of considering failure situation,task completion qualities and pilot workload are also taken into account.Finally,the proposed approach is validated using numerical simulation experiments with two types of scenarios:lower actuator rate limits and airframe damages.The Inceptor Peak Power-Phase(IPPP)metric is adopted to analyze the human-vehicle system simulation results.Results and analysis show that the DDPG based sharing control approach can well address nonlinear PIO problems consisting of Categories Ⅱ and Ⅲ PIO events.展开更多
The theory of shared control combines organically the control of every controlled ele-ments and with it all the controlled elements share the same control element. Applying theschemes of shared control searched by ass...The theory of shared control combines organically the control of every controlled ele-ments and with it all the controlled elements share the same control element. Applying theschemes of shared control searched by assembly programs, an integrated control of all the ele-ments is fulfilled. The distinguishing point of the method is that the maximum control output canbe obtained with the least input information. Hence it is the optimum for the conversion of com-bination states. Finally, a thared rotary valve is designed, and it is the simplest with only onegroup of control holes.展开更多
Shared control with multiple functions of myoelectric prosthetic hand enables individuals with amputation to achieve more precise control with less fatigue,which improves the acceptance of myoelectric prosthetic hands...Shared control with multiple functions of myoelectric prosthetic hand enables individuals with amputation to achieve more precise control with less fatigue,which improves the acceptance of myoelectric prosthetic hands.In this paper,we propose introducing two new functions for prosthetic hands enabled by a shared control based on fingertip tactile sensing:multi-stage grasping and force level switching.A user study involving eight able-bodied and three amputee participants is conducted to assess the performance of our proposed functions in selected common daily life tasks.The purpose of the assessment is to determine if the proposed functions based on tactile sensing can improve the objective performance of the prosthetic hand as well as the subjective experience of users.The results demonstrate the potential benefit of our proposed functions,allowing for faster completion of multiple objects grasp-and-place tasks compared to existing myoelectric control,as well as a higher success rate of force adjustment tasks.Moreover,the tactile-based shared control with our proposed functions reduces muscle use and obtains positive user feedback.展开更多
A concurrency control mechanism for collaborative work is akey element in a mixed reality environment. However, conventional lockingmechanisms restrict potential tasks or the support of non-owners, thusincreasing the ...A concurrency control mechanism for collaborative work is akey element in a mixed reality environment. However, conventional lockingmechanisms restrict potential tasks or the support of non-owners, thusincreasing the working time because of waiting to avoid conflicts. Herein, wepropose an adaptive concurrency control approach that can reduce conflictsand work time. We classify shared object manipulation in mixed reality intodetailed goals and tasks. Then, we model the relationships among goal,task, and ownership. As the collaborative work progresses, the proposedsystem adapts the different concurrency control mechanisms of shared objectmanipulation according to the modeling of goal–task–ownership. With theproposed concurrency control scheme, users can hold shared objects andmove and rotate together in a mixed reality environment similar to realindustrial sites. Additionally, this system provides MS Hololens and Myosensors to recognize inputs from a user and provides results in a mixed realityenvironment. The proposed method is applied to install an air conditioneras a case study. Experimental results and user studies show that, comparedwith the conventional approach, the proposed method reduced the number ofconflicts, waiting time, and total working time.展开更多
Controlling terahertz(THz)polarization with high stability and tunability is essential for achieving further progress in ultrafast spectroscopy,structured-light manipulation,and quantum information processing.Here,we ...Controlling terahertz(THz)polarization with high stability and tunability is essential for achieving further progress in ultrafast spectroscopy,structured-light manipulation,and quantum information processing.Here,we propose a magnetized plasma platform for dynamic THz polarization control by exploiting the intrinsic birefringence between extraordinary and ordinary modes.We identify a strong-magnetization,zero-group-velocity-mismatch regime where the two modes share matched group velocities while retaining finite phase birefringence,enabling robust,phase-stable spin angular momentum control.By tuning the plasma length and magnetic field,we realize programmable phase retardation and demonstrate universal single-qubit gates through parameterized unitary operations.Full-wave particle-in-cell simulations validate high-fidelity polarization transformations across the Poincarésphere and demonstrate the potential for generating structured vector beams under spatially varying magnetic fields.The platform offers ultrafast response,resilience to extreme THz intensities,and in situ tunability,positioning magnetized plasmas as a versatile and damage-resilient medium for next-generation THz polarization control and structured-wave applications.展开更多
Intelligent vehicle(Ⅳ)technology has developed rapidly in recent years.However,achieving fully unmanned driving still presents numerous challenges,which means that human drivers will continue to play a vital role in ...Intelligent vehicle(Ⅳ)technology has developed rapidly in recent years.However,achieving fully unmanned driving still presents numerous challenges,which means that human drivers will continue to play a vital role in vehicle operation for the foreseeable future.Human-machine shared driving,involving cooperation between a human driver and an automated driving system(AVS),has been widely regarded as a necessary stage for the development of IVs.Focusing onⅣdriving safety,this study proposed a human-machine shared lateral control strategy(HSLCS)based on the reliability of driver risk perception.The HSLCS starts by identifying the effective areas of driver risk perception based on eye movements.It establishes an anisotropic driving risk field,which serves as the foundation for the AVS to assess risk levels.Building upon the cumulative and diminishing effects of risk perception,the proposed approach leverages the driver's risk perception effective area and converts the risk field into a representation aligned with the driver's perspective.Subsequently,it quantifies the reliability of the driver's risk perception by using area-matching rules.Finally,based on the driver’s risk perception reliability and dif-ferences in lateral driving operation between the human driver and the AVS,the dynamic distribution of driving authority is achieved through a fuzzy rule-based system,and the human-machine shared lateral control is completed by using model predictive control.The HSLCS was tested across various scenarios on a driver-in-the-loop test platform.The results show that the HSLCS can realize the synergy and complementarity of human and machine intelligence,effectively ensuring the safety ofⅣoperation.展开更多
基金supported by the National Key R&D Program of China(Grant No.2018YFB1307201)the National Natural Science Foundation of China(Grant No.51675123)the Postdoctoral Scientific Research Development Fund(Grant No.LBH-W18058)。
文摘At present,albeit the dexterous hand prostheses of multiple degrees of freedom(DOFs)have become prosperous on the market,the user’s demand on intuitively operating these devices have not been well addressed so that their acceptance rate is relatively low.The unintuitive control method and inadequate sensory feedback are frequently cited as the two barriers to the successful application of these dexterous products.Recently,driven by the wave of artificial intelligence(AI),a series of shared control methods have emerged,in which"bodily function"(myoelectric control)and"artificial intelligence"(local autonomy,computer vision,etc.)are tightly integrated,and provided a new conceptual solution for the intuitive operation of dexterous prostheses.In this paper,the background and development trends of this type of methods are described in detail,and the potential development directions and the key technologies that need breakthroughs are indicated.In practice,we instantiate this shared control strategy by proposing a new method combining simultaneous myoelectric control,multi-finger grasp autonomy,and augmented reality(AR)feedback together.This method"divides"the human sophisticated reach-and-grasp task into several subtasks,and then"conquers"them by using different strategies from either human or machine perspective.It is highly expected that the shared control methods with hybrid human-machine intelligence could address the control problem of dexterous prostheses.
基金supported by China Postdoctoral Science Foundation(Project ID:2024M762602)the National Natural Science Foundation of China under Grant No.62306232Natural Science Basic Research Program of Shaanxi Province under Grant No.2023-JC-QN-0662.
文摘This paper presents an optimized shared control algorithm for human–AI interaction, implemented through a digital twin framework where the physical system and human operator act as the real agent while an AI-driven digital system functions as the virtual agent. In this digital twin architecture, the real agent acquires an optimal control strategy through observed actions, while the AI virtual agent mirrors the real agent to establish a digital replica system and corresponding control policy. Both the real and virtual optimal controllers are approximated using reinforcement learning(RL) techniques. Specifically, critic neural networks(NNs) are employed to learn the virtual and real optimal value functions, while actor NNs are trained to derive their respective optimal controllers. A novel shared mechanism is introduced to integrate both virtual and real value functions into a unified learning framework, yielding an optimal shared controller. This controller adaptively adjusts the confidence ratio between virtual and real agents, enhancing the system's efficiency and flexibility in handling complex control tasks. The stability of the closed-loop system is rigorously analyzed using the Lyapunov method. The effectiveness of the proposed AI–human interactive system is validated through two numerical examples: a representative nonlinear system and an unmanned aerial vehicle(UAV) control system.
文摘Shared control schemes allow a human driver to work with an automated driving agent in driver-vehicle systems while retaining the driver’s abilities to control.The human driver,as an essential agent in the driver-vehicle shared control systems,should be precisely modeled regarding their cognitive processes,control strategies,and decision-making processes.The interactive strategy design between drivers and automated driving agents brings an excellent challenge for human-centric driver assistance systems due to the inherent characteristics of humans.Many open-ended questions arise,such as what proper role of human drivers should act in a shared control scheme?How to make an intelligent decision capable of balancing the benefits of agents in shared control systems?Due to the advent of these attentions and questions,it is desirable to present a survey on the decision making between human drivers and highly automated vehicles,to understand their architectures,human driver modeling,and interaction strategies under the driver-vehicle shared schemes.Finally,we give a further discussion on the key future challenges and opportunities.They are likely to shape new potential research directions.
文摘A shared control of highly automated Steer-by-Wire system is proposed for cooperative driving between the driver and vehicle in the face of driver's abnormal driving. A fault detection scheme is designed to detect the abnormal driving behaviour and transfer the control of the car to the automatic system designed based on a fault tolerant model predictive control(MPC) controller driving the vehicle along an optimal safe path.The proposed concept and control algorithm are tested in a number of scenarios representing intersection, lane change and different types of driver's abnormal behaviour. The simulation results show the feasibility and effectiveness of the proposed method.
基金supported by the National Natural Science Foundation of China(U1664263)。
文摘Due to the critical defects of techniques in fully autonomous vehicles,man-machine cooperative driving is still of great significance in today’s transportation system.Unlike the previous shared control structure,this paper introduces a double loop structure which is applied to indirect shared steering control between driver and automation.In contrast to the tandem indirect shared control,the parallel indirect shared control put the authority allocation system of steering angle into the framework to allocate the corresponding weighting coefficients reasonably and output the final desired steering angle according to the current deviation of vehicle and the accuracy of steering angles.Besides,the active disturbance rejection controller(ADRC)is also added in the frame in order to track the desired steering angle fleetly and accurately as well as restrain the internal and external disturbances effectively which including the steering friction torque,wind speed and ground interference etc.Eventually,we validated the advantages of double loop framework through three sets of double lane change and slalom experiments,respectively.Exactly as we expected,the simulation results show that the double loop structure can effectively reduce the lateral displacement error caused by the driver or the controller,significantly improve the tracking precision and keep great performance in trajectory tracking characteristics when driving errors occur in one of driver and controller.
基金Supported by Defense Industrial Technology Development Program.
文摘The driver-automation shared driving is a transition to fully-autonomous driving,in which human driver and vehicular controller cooperatively share the control authority.This paper investigates the shared steering control of semi-autonomous vehicles with uncertainty from imprecise parameter.By considering driver’s lane-keeping behavior on the vehicle system,a driver-automation shared driving model is introduced for control purpose.Based on the interval type-2(IT2)fuzzy theory,moreover,the driver-automation shared driving model with uncertainty from imprecise parameter is described using an IT2 fuzzy model.After that,the corresponding IT2 fuzzy controller is designed and a direct Lyapunov method is applied to analyze the system stability.In this work,sufficient design conditions in terms of linear matrix inequalities are derived,to guarantee the closed-loop stability of the driver-automation shared control system.In addition,an H∞performance is studied to ensure the robustness of control system.Finally,simulation-based results are provided to demonstrate the performance of proposed control method.Furthermore,an existing type-1 fuzzy controller is introduced as comparison to verify the superiority of the proposed IT2 fuzzy controller.
基金the National Natural Science Foundation of China(No.51775331)。
文摘Contemporary autonomous-driving technology relies on good environmental-perception systems and high-precision maps.For unknown environments or scenarios where perception fails,a human-in-the-loop remote-driving system can effectively complement common solutions,although safety remains an issue for its application.A haptic shared-control algorithm based on non-cooperative game theory is presented in this paper.The algorithm generates collision-free reference paths with model predictive control and predicts the driver’s path using a two-point preview model.Man-machine torque interaction is modeled as a Nash game,and the assist system’s degree of intervention is regulated in real time,according to assessments of collision risk and the driver’s concentration.Simulations of several representative scenarios demonstrate how the proposed method improves driving safety,while respecting driver decisions.
基金co-supported by the Fundamental Research Funds for the Central Universities of China(No.YWF-23-SDHK-L-005)the 1912 Project,China and the Aeronautical Science Foundation of China(No.20220048051001).
文摘This paper presents a Shared Control Architecture(SCA)between a human pilot and a smart inceptor for nonlinear Pilot Induced Oscillations(PIOs),e.g.,category II or III PIOs.One innovation of this paper is that an intelligent shared control architecture is developed based on the intelligent active inceptor technique,i.e.,Smart Adaptive Flight Effective Cue(SAFE-Cue).A deep reinforcement learning approach namely Deep Deterministic Policy Gradient(DDPG)method is chosen to design a gain adaptation mechanism for the SAFE-Cue module.By doing this,the gains of the SAFE-Cue will be intelligently tuned once nonlinear PIOs triggered;meanwhile,the human pilot will receive a force cue from the SAFE-Cue,and will consequently adapting his/her control policy.The second innovation of this paper is that the reward function of the DDPG based gain adaptation approach is constructed according to flying qualities.Under the premise of considering failure situation,task completion qualities and pilot workload are also taken into account.Finally,the proposed approach is validated using numerical simulation experiments with two types of scenarios:lower actuator rate limits and airframe damages.The Inceptor Peak Power-Phase(IPPP)metric is adopted to analyze the human-vehicle system simulation results.Results and analysis show that the DDPG based sharing control approach can well address nonlinear PIO problems consisting of Categories Ⅱ and Ⅲ PIO events.
文摘The theory of shared control combines organically the control of every controlled ele-ments and with it all the controlled elements share the same control element. Applying theschemes of shared control searched by assembly programs, an integrated control of all the ele-ments is fulfilled. The distinguishing point of the method is that the maximum control output canbe obtained with the least input information. Hence it is the optimum for the conversion of com-bination states. Finally, a thared rotary valve is designed, and it is the simplest with only onegroup of control holes.
基金supported by the National Natural Science Foundation of China(Nos.62173197 and 62073249)the Beijing Natural Science Foundation(No.L222012).
文摘Shared control with multiple functions of myoelectric prosthetic hand enables individuals with amputation to achieve more precise control with less fatigue,which improves the acceptance of myoelectric prosthetic hands.In this paper,we propose introducing two new functions for prosthetic hands enabled by a shared control based on fingertip tactile sensing:multi-stage grasping and force level switching.A user study involving eight able-bodied and three amputee participants is conducted to assess the performance of our proposed functions in selected common daily life tasks.The purpose of the assessment is to determine if the proposed functions based on tactile sensing can improve the objective performance of the prosthetic hand as well as the subjective experience of users.The results demonstrate the potential benefit of our proposed functions,allowing for faster completion of multiple objects grasp-and-place tasks compared to existing myoelectric control,as well as a higher success rate of force adjustment tasks.Moreover,the tactile-based shared control with our proposed functions reduces muscle use and obtains positive user feedback.
基金supported by“Regional Innovation Strategy (RIS)”through the National Research Foundation of Korea (NRF)funded by the Ministry of Education (MOE) (2021RIS-004).
文摘A concurrency control mechanism for collaborative work is akey element in a mixed reality environment. However, conventional lockingmechanisms restrict potential tasks or the support of non-owners, thusincreasing the working time because of waiting to avoid conflicts. Herein, wepropose an adaptive concurrency control approach that can reduce conflictsand work time. We classify shared object manipulation in mixed reality intodetailed goals and tasks. Then, we model the relationships among goal,task, and ownership. As the collaborative work progresses, the proposedsystem adapts the different concurrency control mechanisms of shared objectmanipulation according to the modeling of goal–task–ownership. With theproposed concurrency control scheme, users can hold shared objects andmove and rotate together in a mixed reality environment similar to realindustrial sites. Additionally, this system provides MS Hololens and Myosensors to recognize inputs from a user and provides results in a mixed realityenvironment. The proposed method is applied to install an air conditioneras a case study. Experimental results and user studies show that, comparedwith the conventional approach, the proposed method reduced the number ofconflicts, waiting time, and total working time.
基金supported by the National Natural Science Foundation of China (Grant Nos. 12175058 and 11921006)the National Grand Instrument Project (No. 2019YFF01014402)the Beijing Distinguished Young Scientist Program and National Grand Instrument Project No. SQ2019YFF01014400
文摘Controlling terahertz(THz)polarization with high stability and tunability is essential for achieving further progress in ultrafast spectroscopy,structured-light manipulation,and quantum information processing.Here,we propose a magnetized plasma platform for dynamic THz polarization control by exploiting the intrinsic birefringence between extraordinary and ordinary modes.We identify a strong-magnetization,zero-group-velocity-mismatch regime where the two modes share matched group velocities while retaining finite phase birefringence,enabling robust,phase-stable spin angular momentum control.By tuning the plasma length and magnetic field,we realize programmable phase retardation and demonstrate universal single-qubit gates through parameterized unitary operations.Full-wave particle-in-cell simulations validate high-fidelity polarization transformations across the Poincarésphere and demonstrate the potential for generating structured vector beams under spatially varying magnetic fields.The platform offers ultrafast response,resilience to extreme THz intensities,and in situ tunability,positioning magnetized plasmas as a versatile and damage-resilient medium for next-generation THz polarization control and structured-wave applications.
基金supported by the National Natural Science Foundation of China under Grant 52172386the National Natural Science Foundation of China under Grant U22A20247+1 种基金the Jilin Province Science and Technology Development Plan Projects under Grant 20210101057JCthe Jilin Provincial Department of Science and Technology under Grant 20220301009GX.
文摘Intelligent vehicle(Ⅳ)technology has developed rapidly in recent years.However,achieving fully unmanned driving still presents numerous challenges,which means that human drivers will continue to play a vital role in vehicle operation for the foreseeable future.Human-machine shared driving,involving cooperation between a human driver and an automated driving system(AVS),has been widely regarded as a necessary stage for the development of IVs.Focusing onⅣdriving safety,this study proposed a human-machine shared lateral control strategy(HSLCS)based on the reliability of driver risk perception.The HSLCS starts by identifying the effective areas of driver risk perception based on eye movements.It establishes an anisotropic driving risk field,which serves as the foundation for the AVS to assess risk levels.Building upon the cumulative and diminishing effects of risk perception,the proposed approach leverages the driver's risk perception effective area and converts the risk field into a representation aligned with the driver's perspective.Subsequently,it quantifies the reliability of the driver's risk perception by using area-matching rules.Finally,based on the driver’s risk perception reliability and dif-ferences in lateral driving operation between the human driver and the AVS,the dynamic distribution of driving authority is achieved through a fuzzy rule-based system,and the human-machine shared lateral control is completed by using model predictive control.The HSLCS was tested across various scenarios on a driver-in-the-loop test platform.The results show that the HSLCS can realize the synergy and complementarity of human and machine intelligence,effectively ensuring the safety ofⅣoperation.
