The tire-road friction coefficient(TRFC)plays a critical role in vehicle safety and dynamic stability,with model-based approaches being the primary method for TRFC estimation.However,the accuracy of these methods is o...The tire-road friction coefficient(TRFC)plays a critical role in vehicle safety and dynamic stability,with model-based approaches being the primary method for TRFC estimation.However,the accuracy of these methods is often constrained by the complexity of tire force expressions and uncertainties in tire model parameters,particularly under diverse and complex driving conditions.To address these challenges,this paper proposes a novel data enforced unscented Kalman filter(DeUKF)approach for precise TRFC estimation in intelligent chassis systems.First,an Unscented Kalman Filter is constructed using a nominal tire model-based vehicle dynamics formulation.Then,leveraging Willems’Fundamental Lemma and historical real-world driving data,the vehicle dynamics model is adap-tively corrected within the Unscented Kalman Filter framework.This correction effectively mitigates the adverse effects of tire model uncertainties,thereby enhancing TRFC estimation accuracy.Finally,real vehicle experiments are conducted to validate the effectiveness and superiority of the proposed method.展开更多
microRNAs （miRNAs） play an important regulatory role in the self-renewal and differentiation of stem cells. In this study, we examined the effects of miRNA-124 （miR-124） overexpression in bone marrow-derived mesen...microRNAs （miRNAs） play an important regulatory role in the self-renewal and differentiation of stem cells. In this study, we examined the effects of miRNA-124 （miR-124） overexpression in bone marrow-derived mesenchymal stem cells. In particular, we focused on the effect of overexpression on the differentiation of bone marrow-derived mesenchymal stem cells into neurons. First, we used GeneChip technology to analyze the expression of miRNAs in bone marrow-derived mesen- chymal stem cells, neural stem cells and neurons, miR-124 expression was substantially reduced in bone marrow-derived mesenchymal stem cells compared with the other cell types. We con- structed a lentiviral vector overexpressing miR-124 and transfected it into bone marrow-derived mesenchymal stem cells. Intracellular expression levels of the neuronal early markers [3-III tu- bulin and microtubule-associated protein-2 were significantly increased, and apoptosis induced by oxygen and glucose deprivation was reduced in transfected cells. After miR-124-transfected bone marrow-derived mesenchymal stem cells were transplanted into the injured rat spinal cord, a large number of cells positive for the neuronal marker neurofilament-200 were observed in the transplanted region. The Basso-Beattie-Bresnahan locomotion scores showed that the motor function of the hind limb of rats with spinal cord injury was substantially improved. These re- sults suggest that miR-124 plays an important role in the differentiation of bone marrow-derived mesenchymal stem cells into neurons. Our findings should facilitate the development of novel strategies for enhancing the therapeutic efficacy of bone marrow-derived mesenchymal stem cell transplantation for spinal cord injury.展开更多
Lane detection is a fundamental aspect of most current advanced driver assistance systems(ADASs). A large number of existing results focus on the study of vision-based lane detection methods due to the extensive knowl...Lane detection is a fundamental aspect of most current advanced driver assistance systems(ADASs). A large number of existing results focus on the study of vision-based lane detection methods due to the extensive knowledge background and the low-cost of camera devices. In this paper, previous visionbased lane detection studies are reviewed in terms of three aspects, which are lane detection algorithms, integration, and evaluation methods. Next, considering the inevitable limitations that exist in the camera-based lane detection system, the system integration methodologies for constructing more robust detection systems are reviewed and analyzed. The integration methods are further divided into three levels, namely, algorithm, system,and sensor. Algorithm level combines different lane detection algorithms while system level integrates other object detection systems to comprehensively detect lane positions. Sensor level uses multi-modal sensors to build a robust lane recognition system. In view of the complexity of evaluating the detection system, and the lack of common evaluation procedure and uniform metrics in past studies, the existing evaluation methods and metrics are analyzed and classified to propose a better evaluation of the lane detection system. Next, a comparison of representative studies is performed. Finally, a discussion on the limitations of current lane detection systems and the future developing trends toward an Artificial Society, Computational experiment-based parallel lane detection framework is proposed.展开更多
In present-day highly-automated vehicles,there are occasions when the driving system disengages and the human driver is required to take-over.This is of great importance to a vehicle's safety and ride comfort.In t...In present-day highly-automated vehicles,there are occasions when the driving system disengages and the human driver is required to take-over.This is of great importance to a vehicle's safety and ride comfort.In the U.S state of California,the Autonomous Vehicle Testing Regulations require every manufacturer testing autonomous vehicles on public roads to submit an annual report summarizing the disengagements of the technology experienced during testing.On 1 January 2016,seven manufacturers submitted their first disengagement reports:Bosch,Delphi,Google,Nissan,Mercedes-Benz,Volkswagen,and Tesla Motors.This work analyses the data from these disengagement reports with the aim of gaining abetter understanding of the situations in which a driver is required to takeover,as this is potentially useful in improving the Society of Automotive Engineers(SAE)Level 2 and Level 3 automation technologies.Disengagement events from testing are classified into different groups based on attributes and the causes of disengagement are investigated and compared in detail.The mechanisms and time taken for take-over transition occurred in disengagements are studied.Finally,recommendations for OEMs,manufacturers,and government organizations are also discussed.展开更多
Next-generation vehicle control and future autonomous driving require further advances in vehicle dynamic state estimation. This article provides a concise review, along with the perspectives, of the recent developmen...Next-generation vehicle control and future autonomous driving require further advances in vehicle dynamic state estimation. This article provides a concise review, along with the perspectives, of the recent developments in the estimation of vehicle dynamic states. The definitions used in vehicle dynamic state estimation are first introduced, and alternative estimation structures are presented. Then, the sensor configuration schemes used to estimate vehicle velocity, sideslip angle, yaw rate and roll angle are presented. The vehicle models used for vehicle dynamic state estimation are further summarized, and representative estimation approaches are discussed. Future concerns and perspectives for vehicle dynamic state estimation are also discussed.展开更多
Knowledge graphs(KGs)have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based services.In recent years,researchers in the field of power system...Knowledge graphs(KGs)have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based services.In recent years,researchers in the field of power systems have explored KGs to develop intelligent dispatching systems for increasingly large power grids.With multiple power grid dispatching knowledge graphs(PDKGs)constructed by different agencies,the knowledge fusion of different PDKGs is useful for providing more accurate decision supports.To achieve this,entity alignment that aims at connecting different KGs by identifying equivalent entities is a critical step.Existing entity alignment methods cannot integrate useful structural,attribute,and relational information while calculating entities’similarities and are prone to making many-to-one alignments,thus can hardly achieve the best performance.To address these issues,this paper proposes a collective entity alignment model that integrates three kinds of available information and makes collective counterpart assignments.This model proposes a novel knowledge graph attention network(KGAT)to learn the embeddings of entities and relations explicitly and calculates entities’similarities by adaptively incorporating the structural,attribute,and relational similarities.Then,we formulate the counterpart assignment task as an integer programming(IP)problem to obtain one-to-one alignments.We not only conduct experiments on a pair of PDKGs but also evaluate o ur model on three commonly used cross-lingual KGs.Experimental comparisons indicate that our model outperforms other methods and provides an effective tool for the knowledge fusion of PDKGs.展开更多
The changes of blood perfusion of contralateral testis after unilateral testicular torsion remain controversial.In this study,28 New Zealand white male rabbits were randomly divided into five groups.Group A(n=8),the c...The changes of blood perfusion of contralateral testis after unilateral testicular torsion remain controversial.In this study,28 New Zealand white male rabbits were randomly divided into five groups.Group A(n=8),the control group,underwent a sham operation on the unilateral testis without inducing testicular torsion.In groups B,C,and D(n=5 each),unilateral testicular torsion was induced,and,after 3,6 or 24 h,respectively,detorsion was performed.In group E(n=5),permanent unilateral testicular torsion was applied.Contrast-enhanced ultrasound was used to observe the blood perfusion of the contralateral testis at the following stages:pre-torsion(preopration),immediately post-torsion(postopration),pre-detorsion,immediately post-detorsion,and late-stage post-detorsion(6-12 h post-detorsion in groups B-D)or at a similar time point(15-21 h post-torsion in group E).Time-intensity curves were generated,and the following parameters were derived and analyzed:arrival time,time to peak intensity,peak intensity,and half-time of the descending peak intensity.The analysis revealed that blood perfusion of the contralateral testis increased immediately after testicular torsion on the opposite side(P<0.05),which increased with prolonged testicular torsion of the other testis.This research demonstrated that contrast-enhanced ultrasound was valuable in evaluating blood perfusion of the contralateral testis after unilateral testicular torsion.展开更多
Prompted by emerging developments in connected and automated vehicles, parallel steering control, one aspect of parallel driving, has become highly important for intelligent vehicles for easing the burden and ensuring...Prompted by emerging developments in connected and automated vehicles, parallel steering control, one aspect of parallel driving, has become highly important for intelligent vehicles for easing the burden and ensuring the safety of human drivers. This paper presents a parallel steering control framework for an intelligent vehicle using moving horizon optimization.The framework considers lateral stability, collision avoidance and actuator saturation and describes them as constraints, which can blend the operation of a human driver and a parallel steering controller effectively. Moreover, the road hazard and the steering operation error are employed to evaluate the operational hazardous of an intelligent vehicle. Under the hazard evaluation,the intelligent vehicle will be mainly operated by the human driver when the vehicle operates in a safe and stable manner.The automated steering driving objective will play an active role and regulate the steering operations of the intelligent vehicle based on the hazard evaluation. To verify the effectiveness of the proposed hazard-evaluation-oriented moving horizon parallel steering control approach, various validations are conducted, and the results are compared with a parallel steering scheme that does not consider automated driving situations. The results illustrate that the proposed parallel steering controller achieves acceptable performance under both conventional conditions and hazardous conditions.展开更多
In today's modern electric vehicles,enhancing the safety-critical cyber-physical system(CPS)'s performance is necessary for the safe maneuverability of the vehicle.As a typical CPS,the braking system is crucia...In today's modern electric vehicles,enhancing the safety-critical cyber-physical system(CPS)'s performance is necessary for the safe maneuverability of the vehicle.As a typical CPS,the braking system is crucial for the vehicle design and safe control.However,precise state estimation of the brake pressure is desired to perform safe driving with a high degree of autonomy.In this paper,a sensorless state estimation technique of the vehicle's brake pressure is developed using a deep-learning approach.A deep neural network(DNN)is structured and trained using deep-learning training techniques,such as,dropout and rectified units.These techniques are utilized to obtain more accurate model for brake pressure state estimation applications.The proposed model is trained using real experimental training data which were collected via conducting real vehicle testing.The vehicle was attached to a chassis dynamometer while the brake pressure data were collected under random driving cycles.Based on these experimental data,the DNN is trained and the performance of the proposed state estimation approach is validated accordingly.The results demonstrate high-accuracy brake pressure state estimation with RMSE of 0.048 MPa.展开更多
While autonomous vehicles are vital components of intelligent transportation systems,ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving.Therefore,we present...While autonomous vehicles are vital components of intelligent transportation systems,ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving.Therefore,we present a novel robust reinforcement learning approach with safety guarantees to attain trustworthy decision-making for autonomous vehicles.The proposed technique ensures decision trustworthiness in terms of policy robustness and collision safety.Specifically,an adversary model is learned online to simulate the worst-case uncertainty by approximating the optimal adversarial perturbations on the observed states and environmental dynamics.In addition,an adversarial robust actor-critic algorithm is developed to enable the agent to learn robust policies against perturbations in observations and dynamics.Moreover,we devise a safety mask to guarantee the collision safety of the autonomous driving agent during both the training and testing processes using an interpretable knowledge model known as the Responsibility-Sensitive Safety Model.Finally,the proposed approach is evaluated through both simulations and experiments.These results indicate that the autonomous driving agent can make trustworthy decisions and drastically reduce the number of collisions through robust safety policies.展开更多
Based on the principle of speed superposition,the oscillation system for continuous casting mold synchronously driven by double servomotors can adjust all parameters online with high reliability and high bearing capac...Based on the principle of speed superposition,the oscillation system for continuous casting mold synchronously driven by double servomotors can adjust all parameters online with high reliability and high bearing capacity.The elastodynamic model of the oscillation system was presented to analyze the oscillation characteristics affected by multiple parameters.Firstly,the working principle of the system was illustrated,with the kinematic model of the system given.Considering the elastic deformation of mechanical components,the lumped parameter method was used to establish the equivalent component and elastodynamic model of the oscillation system.Based on Runge–Kutta method,the oscillation response of the system was calculated.The validity of the elastodynamic model was validated by comparing the simulated solution and experimental data,and the oscillation characteristics of the system were analyzed emphatically with model parameters.The results showed that the first three harmonic frequencies of the eccentric shafts are the main cause of harmonic resonance,which has a strong relationship with the system error.The increase in the fluctuation coefficients and the decrease in the phase difference enhanced the elastic oscillation of the system.System elastic error tends to show linear growth with the increase in the friction force of the slab.展开更多
Dear Editor, With the development of automobile industry and artificial intelligence(AI) domains, autonomous vehicles(AVs) are becoming a reality and promise to revolutionize human mobility [1]–[3]. The decision-maki...Dear Editor, With the development of automobile industry and artificial intelligence(AI) domains, autonomous vehicles(AVs) are becoming a reality and promise to revolutionize human mobility [1]–[3]. The decision-making system of AVs is crucial, which is typically required to trade off multiple competing objectives. For example,when determining driving policies.展开更多
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 f...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 Citation:L.Vlacic,H.Huang,M.Dotoli,Y.Wang,P.Ioanno,L.Fan,X.Wang,R.Carli,C.Lv,L.Li,X.Na,Q.-L.Han,and F.-Y.Wang,“Automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA J.Autom.Sinica,vol.11,no.8,pp.1723-1727,Aug.2024.展开更多
基金Supported by the China’s National Key R&D Program(Grant No.2022YFB2503103).
文摘The tire-road friction coefficient(TRFC)plays a critical role in vehicle safety and dynamic stability,with model-based approaches being the primary method for TRFC estimation.However,the accuracy of these methods is often constrained by the complexity of tire force expressions and uncertainties in tire model parameters,particularly under diverse and complex driving conditions.To address these challenges,this paper proposes a novel data enforced unscented Kalman filter(DeUKF)approach for precise TRFC estimation in intelligent chassis systems.First,an Unscented Kalman Filter is constructed using a nominal tire model-based vehicle dynamics formulation.Then,leveraging Willems’Fundamental Lemma and historical real-world driving data,the vehicle dynamics model is adap-tively corrected within the Unscented Kalman Filter framework.This correction effectively mitigates the adverse effects of tire model uncertainties,thereby enhancing TRFC estimation accuracy.Finally,real vehicle experiments are conducted to validate the effectiveness and superiority of the proposed method.
基金supported by the National Natural Science Foundation of China,No.81070971
文摘microRNAs （miRNAs） play an important regulatory role in the self-renewal and differentiation of stem cells. In this study, we examined the effects of miRNA-124 （miR-124） overexpression in bone marrow-derived mesenchymal stem cells. In particular, we focused on the effect of overexpression on the differentiation of bone marrow-derived mesenchymal stem cells into neurons. First, we used GeneChip technology to analyze the expression of miRNAs in bone marrow-derived mesen- chymal stem cells, neural stem cells and neurons, miR-124 expression was substantially reduced in bone marrow-derived mesenchymal stem cells compared with the other cell types. We con- structed a lentiviral vector overexpressing miR-124 and transfected it into bone marrow-derived mesenchymal stem cells. Intracellular expression levels of the neuronal early markers [3-III tu- bulin and microtubule-associated protein-2 were significantly increased, and apoptosis induced by oxygen and glucose deprivation was reduced in transfected cells. After miR-124-transfected bone marrow-derived mesenchymal stem cells were transplanted into the injured rat spinal cord, a large number of cells positive for the neuronal marker neurofilament-200 were observed in the transplanted region. The Basso-Beattie-Bresnahan locomotion scores showed that the motor function of the hind limb of rats with spinal cord injury was substantially improved. These re- sults suggest that miR-124 plays an important role in the differentiation of bone marrow-derived mesenchymal stem cells into neurons. Our findings should facilitate the development of novel strategies for enhancing the therapeutic efficacy of bone marrow-derived mesenchymal stem cell transplantation for spinal cord injury.
文摘Lane detection is a fundamental aspect of most current advanced driver assistance systems(ADASs). A large number of existing results focus on the study of vision-based lane detection methods due to the extensive knowledge background and the low-cost of camera devices. In this paper, previous visionbased lane detection studies are reviewed in terms of three aspects, which are lane detection algorithms, integration, and evaluation methods. Next, considering the inevitable limitations that exist in the camera-based lane detection system, the system integration methodologies for constructing more robust detection systems are reviewed and analyzed. The integration methods are further divided into three levels, namely, algorithm, system,and sensor. Algorithm level combines different lane detection algorithms while system level integrates other object detection systems to comprehensively detect lane positions. Sensor level uses multi-modal sensors to build a robust lane recognition system. In view of the complexity of evaluating the detection system, and the lack of common evaluation procedure and uniform metrics in past studies, the existing evaluation methods and metrics are analyzed and classified to propose a better evaluation of the lane detection system. Next, a comparison of representative studies is performed. Finally, a discussion on the limitations of current lane detection systems and the future developing trends toward an Artificial Society, Computational experiment-based parallel lane detection framework is proposed.
基金supported by Jaguar Land Roverthe UK-EPSRC grant EP/N012089/1 as part of the jointly funded Towards Autonomy:Smart and Connected Control(TASCC)Programme
文摘In present-day highly-automated vehicles,there are occasions when the driving system disengages and the human driver is required to take-over.This is of great importance to a vehicle's safety and ride comfort.In the U.S state of California,the Autonomous Vehicle Testing Regulations require every manufacturer testing autonomous vehicles on public roads to submit an annual report summarizing the disengagements of the technology experienced during testing.On 1 January 2016,seven manufacturers submitted their first disengagement reports:Bosch,Delphi,Google,Nissan,Mercedes-Benz,Volkswagen,and Tesla Motors.This work analyses the data from these disengagement reports with the aim of gaining abetter understanding of the situations in which a driver is required to takeover,as this is potentially useful in improving the Society of Automotive Engineers(SAE)Level 2 and Level 3 automation technologies.Disengagement events from testing are classified into different groups based on attributes and the causes of disengagement are investigated and compared in detail.The mechanisms and time taken for take-over transition occurred in disengagements are studied.Finally,recommendations for OEMs,manufacturers,and government organizations are also discussed.
基金supported by the National Natural Science Foundation of China(61403158,61520106008)the Project of the Education Department of Jilin Province(2016-429)
文摘Next-generation vehicle control and future autonomous driving require further advances in vehicle dynamic state estimation. This article provides a concise review, along with the perspectives, of the recent developments in the estimation of vehicle dynamic states. The definitions used in vehicle dynamic state estimation are first introduced, and alternative estimation structures are presented. Then, the sensor configuration schemes used to estimate vehicle velocity, sideslip angle, yaw rate and roll angle are presented. The vehicle models used for vehicle dynamic state estimation are further summarized, and representative estimation approaches are discussed. Future concerns and perspectives for vehicle dynamic state estimation are also discussed.
基金supported by the National Key R&D Program of China(2018AAA0101502)the Science and Technology Project of SGCC(State Grid Corporation of China):Fundamental Theory of Human-in-the-Loop Hybrid-Augmented Intelligence for Power Grid Dispatch and Control。
文摘Knowledge graphs(KGs)have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based services.In recent years,researchers in the field of power systems have explored KGs to develop intelligent dispatching systems for increasingly large power grids.With multiple power grid dispatching knowledge graphs(PDKGs)constructed by different agencies,the knowledge fusion of different PDKGs is useful for providing more accurate decision supports.To achieve this,entity alignment that aims at connecting different KGs by identifying equivalent entities is a critical step.Existing entity alignment methods cannot integrate useful structural,attribute,and relational information while calculating entities’similarities and are prone to making many-to-one alignments,thus can hardly achieve the best performance.To address these issues,this paper proposes a collective entity alignment model that integrates three kinds of available information and makes collective counterpart assignments.This model proposes a novel knowledge graph attention network(KGAT)to learn the embeddings of entities and relations explicitly and calculates entities’similarities by adaptively incorporating the structural,attribute,and relational similarities.Then,we formulate the counterpart assignment task as an integer programming(IP)problem to obtain one-to-one alignments.We not only conduct experiments on a pair of PDKGs but also evaluate o ur model on three commonly used cross-lingual KGs.Experimental comparisons indicate that our model outperforms other methods and provides an effective tool for the knowledge fusion of PDKGs.
基金We thank the animal facility of Ruijing Hospital(Shanghai,China)for valuable assistance in this study.
文摘The changes of blood perfusion of contralateral testis after unilateral testicular torsion remain controversial.In this study,28 New Zealand white male rabbits were randomly divided into five groups.Group A(n=8),the control group,underwent a sham operation on the unilateral testis without inducing testicular torsion.In groups B,C,and D(n=5 each),unilateral testicular torsion was induced,and,after 3,6 or 24 h,respectively,detorsion was performed.In group E(n=5),permanent unilateral testicular torsion was applied.Contrast-enhanced ultrasound was used to observe the blood perfusion of the contralateral testis at the following stages:pre-torsion(preopration),immediately post-torsion(postopration),pre-detorsion,immediately post-detorsion,and late-stage post-detorsion(6-12 h post-detorsion in groups B-D)or at a similar time point(15-21 h post-torsion in group E).Time-intensity curves were generated,and the following parameters were derived and analyzed:arrival time,time to peak intensity,peak intensity,and half-time of the descending peak intensity.The analysis revealed that blood perfusion of the contralateral testis increased immediately after testicular torsion on the opposite side(P<0.05),which increased with prolonged testicular torsion of the other testis.This research demonstrated that contrast-enhanced ultrasound was valuable in evaluating blood perfusion of the contralateral testis after unilateral testicular torsion.
基金supported by the National Nature Science Foundation of China(61520106008,61790563,U1664263)
文摘Prompted by emerging developments in connected and automated vehicles, parallel steering control, one aspect of parallel driving, has become highly important for intelligent vehicles for easing the burden and ensuring the safety of human drivers. This paper presents a parallel steering control framework for an intelligent vehicle using moving horizon optimization.The framework considers lateral stability, collision avoidance and actuator saturation and describes them as constraints, which can blend the operation of a human driver and a parallel steering controller effectively. Moreover, the road hazard and the steering operation error are employed to evaluate the operational hazardous of an intelligent vehicle. Under the hazard evaluation,the intelligent vehicle will be mainly operated by the human driver when the vehicle operates in a safe and stable manner.The automated steering driving objective will play an active role and regulate the steering operations of the intelligent vehicle based on the hazard evaluation. To verify the effectiveness of the proposed hazard-evaluation-oriented moving horizon parallel steering control approach, various validations are conducted, and the results are compared with a parallel steering scheme that does not consider automated driving situations. The results illustrate that the proposed parallel steering controller achieves acceptable performance under both conventional conditions and hazardous conditions.
文摘In today's modern electric vehicles,enhancing the safety-critical cyber-physical system(CPS)'s performance is necessary for the safe maneuverability of the vehicle.As a typical CPS,the braking system is crucial for the vehicle design and safe control.However,precise state estimation of the brake pressure is desired to perform safe driving with a high degree of autonomy.In this paper,a sensorless state estimation technique of the vehicle's brake pressure is developed using a deep-learning approach.A deep neural network(DNN)is structured and trained using deep-learning training techniques,such as,dropout and rectified units.These techniques are utilized to obtain more accurate model for brake pressure state estimation applications.The proposed model is trained using real experimental training data which were collected via conducting real vehicle testing.The vehicle was attached to a chassis dynamometer while the brake pressure data were collected under random driving cycles.Based on these experimental data,the DNN is trained and the performance of the proposed state estimation approach is validated accordingly.The results demonstrate high-accuracy brake pressure state estimation with RMSE of 0.048 MPa.
基金supported in part by the Start-Up Grant-Nanyang Assistant Professorship Grant of Nanyang Technological Universitythe Agency for Science,Technology and Research(A*STAR)under Advanced Manufacturing and Engineering(AME)Young Individual Research under Grant(A2084c0156)+2 种基金the MTC Individual Research Grant(M22K2c0079)the ANR-NRF Joint Grant(NRF2021-NRF-ANR003 HM Science)the Ministry of Education(MOE)under the Tier 2 Grant(MOE-T2EP50222-0002)。
文摘While autonomous vehicles are vital components of intelligent transportation systems,ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving.Therefore,we present a novel robust reinforcement learning approach with safety guarantees to attain trustworthy decision-making for autonomous vehicles.The proposed technique ensures decision trustworthiness in terms of policy robustness and collision safety.Specifically,an adversary model is learned online to simulate the worst-case uncertainty by approximating the optimal adversarial perturbations on the observed states and environmental dynamics.In addition,an adversarial robust actor-critic algorithm is developed to enable the agent to learn robust policies against perturbations in observations and dynamics.Moreover,we devise a safety mask to guarantee the collision safety of the autonomous driving agent during both the training and testing processes using an interpretable knowledge model known as the Responsibility-Sensitive Safety Model.Finally,the proposed approach is evaluated through both simulations and experiments.These results indicate that the autonomous driving agent can make trustworthy decisions and drastically reduce the number of collisions through robust safety policies.
基金supported by the Joint Fund of Iron and Steel Research of Hebei Province(E2016203339).
文摘Based on the principle of speed superposition,the oscillation system for continuous casting mold synchronously driven by double servomotors can adjust all parameters online with high reliability and high bearing capacity.The elastodynamic model of the oscillation system was presented to analyze the oscillation characteristics affected by multiple parameters.Firstly,the working principle of the system was illustrated,with the kinematic model of the system given.Considering the elastic deformation of mechanical components,the lumped parameter method was used to establish the equivalent component and elastodynamic model of the oscillation system.Based on Runge–Kutta method,the oscillation response of the system was calculated.The validity of the elastodynamic model was validated by comparing the simulated solution and experimental data,and the oscillation characteristics of the system were analyzed emphatically with model parameters.The results showed that the first three harmonic frequencies of the eccentric shafts are the main cause of harmonic resonance,which has a strong relationship with the system error.The increase in the fluctuation coefficients and the decrease in the phase difference enhanced the elastic oscillation of the system.System elastic error tends to show linear growth with the increase in the friction force of the slab.
文摘Dear Editor, With the development of automobile industry and artificial intelligence(AI) domains, autonomous vehicles(AVs) are becoming a reality and promise to revolutionize human mobility [1]–[3]. The decision-making system of AVs is crucial, which is typically required to trade off multiple competing objectives. For example,when determining driving policies.
基金supported in part by the Hong Kong Polytechnic University via the project P0038447The Science and Technology Development Fund,Macao SAR(0093/2023/RIA2)The Science and Technology Development Fund,Macao SAR(0145/2023/RIA3).
文摘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 Citation:L.Vlacic,H.Huang,M.Dotoli,Y.Wang,P.Ioanno,L.Fan,X.Wang,R.Carli,C.Lv,L.Li,X.Na,Q.-L.Han,and F.-Y.Wang,“Automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA J.Autom.Sinica,vol.11,no.8,pp.1723-1727,Aug.2024.
