Vehicle collision avoidance(CA)has been widely studied to improve road traffic safety.However,most evasion assistance control methods face challenges in effectively coordinating collision avoidance safety and human-ma...Vehicle collision avoidance(CA)has been widely studied to improve road traffic safety.However,most evasion assistance control methods face challenges in effectively coordinating collision avoidance safety and human-machine interaction conflict.This paper introduces a novel multi-mode evasion assistance control(MEAC)method for intelligent distributed-drive electric vehicles.A reference safety area is established considering the vehicle safety and stability requirements,which serves as a guiding principle for evading obstacles.The proposed method includes two control modes:Shared-EAC(S-EAC)and Emergency-EAC(E-EAC).In S-EAC,an integrated human-machine authority allocation mechanism is designed to mitigate conflicts between human drivers and the control system during collision avoidance.The E-EAC mode is tailored for situations where the driver has no collision avoidance behavior and utilizes model predictive control to generate additional yaw moments for collision avoidance.Simulation and experimental results indicate that the proposed method reduces human-machine conflict and assists the driver in safe collision avoidance in the S-EAC mode under various driver conditions.In addition,it enhances the vehicle responsiveness and reduces the extent of emergency steering in the E-EAC mode while improving the safety and stability during the collision avoidance process.展开更多
Chassis-by-wire technology has gained significant attention,with the scope of chassis domain control expanding from traditional two-dimensional plane motion control to encompass three-dimensional space motion control....Chassis-by-wire technology has gained significant attention,with the scope of chassis domain control expanding from traditional two-dimensional plane motion control to encompass three-dimensional space motion control.Modern chassis-by-wire systems manage an increasing number of heterogeneous chassis execution systems,including distributed drive,all-wheel drive(AWD),brake-by-wire(BBW),steer-by-wire(SBW),rear-wheel steering(RWS),active stabilizer bar(ASB)and active suspension system(ASS),greatly enhancing the controllable degrees of freedom compared to conventional chassis configurations.To advance research in chassis domain control,it is essential to understand how these heterogeneous execution systems influence vehicle dynamics.This paper focuses on the modeling and analysis of the lateral,longitudinal,and vertical chassis control and execution systems,-as well as their impact on vehicle lateral motion.Using a vehicle simulation platform,both the vehicle dynamics model and the individual dynamics models of each execution system were developed to analyze the influence of these systems on lateral dynamics.Additionally,a hierarchical control architecture was designed to control the vehicle’s lateral stability.The effectiveness of the proposed control scheme was demonstrated and validated through hardware-in-the-loop(HIL)tests and real-world vehicle testing.展开更多
To study the main active components and antioxidant activity in vitro of extracts from Callisia repens , the contents of main active components such as total flavonoids, total anthocyanin and total sugar in the extrac...To study the main active components and antioxidant activity in vitro of extracts from Callisia repens , the contents of main active components such as total flavonoids, total anthocyanin and total sugar in the extracts were studied by spectrophotometry. The components and content of 18 kinds of metals were determined by ICP-MS mass spectrometry. Finally, the oxidative activity of the extract was evaluated by spectrophotometry. Results showed that the content of flavonoids, the total protein, the total sugar and the total anthocyanin in C. repens extract powder were 2.04%, 1.83%, 55.2% and 7.2%, respectively. Beneficial trace elements such as Ca, Mn, Mg in C. repens extracts were higher, while harmful heavy metals such as Pb, Hg, Ag, Co, Ge were very tiny or not detected at all. The IC 50 of C. repens was 0.265 mg/mL for scavenging DPPH·, and 1.16 mg/mL for scavenging ·OH free radical, the total reducing power of 1 mg extract was equivalent to that of 39 μg of Vc, and the extract showed no regular chelating power to ferrous ions. In conclusion, C. repens extracts have high content of natural active components, but extremely low content of the harmful heavy metals, and C. repens extract has good antioxidant capacity. Its antioxidant activity is realized by a variety of active factors through a synergistic mechanism. Thus, C. repens extract has great potential for developing into functional foods.展开更多
The type of road surface condition(RSC)will directly affect the driving performance of vehicles.Monitoring the type of RSC is essential for both transportation agencies and individual drivers.However,most existing met...The type of road surface condition(RSC)will directly affect the driving performance of vehicles.Monitoring the type of RSC is essential for both transportation agencies and individual drivers.However,most existing methods are solely based on a dynamics-based method or an image-based method,which is susceptible to road excitation limitations and interference from the external environment.Therefore,this paper proposes a decision-level fusion identification framework of the RSC based on ego-vehicle trajectory reckoning to accurately obtain the type of RSC that the front wheels of the vehicle will expe-rience.First,a road feature extraction model based on multi-task learning is conducted,which can simultaneously segment the drivable area and road cast shadow.Second,the optimized candidate regions of interest are classified with confidence levels by ShuffleNet.Considering environmental interference,candidate regions of interest regarded as virtual sensors are fused by improved Dempster-Shafer evidence theory to obtain the fusion results.Finally,the ego-vehicle trajectory reckoning module based on the kinematic bicycle model is added to the proposed fusion method to extract the RSC experienced by the front wheels.The performance of the entire framework is verified on a specific dataset with shadow and split curve roads.The results reveal that the proposed method can identify the RSC with accurate predictions in real time.展开更多
Vehicle mass is an important parameter for motion control of intelligent vehicles,but is hard to directly measure using normal sensors.Therefore,accurate estimation of vehicle mass becomes crucial.In this paper,a vehi...Vehicle mass is an important parameter for motion control of intelligent vehicles,but is hard to directly measure using normal sensors.Therefore,accurate estimation of vehicle mass becomes crucial.In this paper,a vehicle mass estimation method based on fusion of machine learning and vehicle dynamic model is introduced.In machine learning method,a feedforward neural network(FFNN)is used to learn the relationship between vehicle mass and other state parameters,namely longitudinal speed and acceleration,driving or braking torque,and wheel angular speed.In dynamics-based method,recursive least square(RLS)with forgetting factor based on vehicle dynamic model is used to estimate the vehicle mass.According to the reliability of each method under different conditions,these two methods are fused using fuzzy logic.Simulation tests under New European Driving Cycle(NEDC)condition are carried out.The simulation results show that the estimation accuracy of the fusion method is around 97%,and that the fusion method performs better stability and robustness compared with each single method.展开更多
基金Supported by National Key Research and Development Program of China(Grant Nos.2022YFE0117100 and 2021YFB250120101)National Natural Science Foundation of China(Grant No.52325212)+1 种基金Shanghai Municipal Automotive Industry Science,Technology Development Foundation(Grant No.2203)the SAIC Motor Corporation Limited(Grant No.2023023).
文摘Vehicle collision avoidance(CA)has been widely studied to improve road traffic safety.However,most evasion assistance control methods face challenges in effectively coordinating collision avoidance safety and human-machine interaction conflict.This paper introduces a novel multi-mode evasion assistance control(MEAC)method for intelligent distributed-drive electric vehicles.A reference safety area is established considering the vehicle safety and stability requirements,which serves as a guiding principle for evading obstacles.The proposed method includes two control modes:Shared-EAC(S-EAC)and Emergency-EAC(E-EAC).In S-EAC,an integrated human-machine authority allocation mechanism is designed to mitigate conflicts between human drivers and the control system during collision avoidance.The E-EAC mode is tailored for situations where the driver has no collision avoidance behavior and utilizes model predictive control to generate additional yaw moments for collision avoidance.Simulation and experimental results indicate that the proposed method reduces human-machine conflict and assists the driver in safe collision avoidance in the S-EAC mode under various driver conditions.In addition,it enhances the vehicle responsiveness and reduces the extent of emergency steering in the E-EAC mode while improving the safety and stability during the collision avoidance process.
基金Supported by National Nature Science Foundation of China(Grant Nos.52325212,52372394)National Key Research and Development Program of China(Grant Nos.2022YFE0117100,2021YFB2501201)+1 种基金Industry-University-Research Innovation Fund for Chinese Universities(Grand No.2024HT010)Fundamental Research Funds for the Central Universities.
文摘Chassis-by-wire technology has gained significant attention,with the scope of chassis domain control expanding from traditional two-dimensional plane motion control to encompass three-dimensional space motion control.Modern chassis-by-wire systems manage an increasing number of heterogeneous chassis execution systems,including distributed drive,all-wheel drive(AWD),brake-by-wire(BBW),steer-by-wire(SBW),rear-wheel steering(RWS),active stabilizer bar(ASB)and active suspension system(ASS),greatly enhancing the controllable degrees of freedom compared to conventional chassis configurations.To advance research in chassis domain control,it is essential to understand how these heterogeneous execution systems influence vehicle dynamics.This paper focuses on the modeling and analysis of the lateral,longitudinal,and vertical chassis control and execution systems,-as well as their impact on vehicle lateral motion.Using a vehicle simulation platform,both the vehicle dynamics model and the individual dynamics models of each execution system were developed to analyze the influence of these systems on lateral dynamics.Additionally,a hierarchical control architecture was designed to control the vehicle’s lateral stability.The effectiveness of the proposed control scheme was demonstrated and validated through hardware-in-the-loop(HIL)tests and real-world vehicle testing.
基金Supported by the Industry-University-Research Project of Fujian Provincial Department of Education(JA15296)the Project of Science and Technology Bureau of Zhangzhou City,Fujian Province(ZZ2018J20)Guiding Project of Fujian Provincial Department of Science and Technology(2019N0018)
文摘To study the main active components and antioxidant activity in vitro of extracts from Callisia repens , the contents of main active components such as total flavonoids, total anthocyanin and total sugar in the extracts were studied by spectrophotometry. The components and content of 18 kinds of metals were determined by ICP-MS mass spectrometry. Finally, the oxidative activity of the extract was evaluated by spectrophotometry. Results showed that the content of flavonoids, the total protein, the total sugar and the total anthocyanin in C. repens extract powder were 2.04%, 1.83%, 55.2% and 7.2%, respectively. Beneficial trace elements such as Ca, Mn, Mg in C. repens extracts were higher, while harmful heavy metals such as Pb, Hg, Ag, Co, Ge were very tiny or not detected at all. The IC 50 of C. repens was 0.265 mg/mL for scavenging DPPH·, and 1.16 mg/mL for scavenging ·OH free radical, the total reducing power of 1 mg extract was equivalent to that of 39 μg of Vc, and the extract showed no regular chelating power to ferrous ions. In conclusion, C. repens extracts have high content of natural active components, but extremely low content of the harmful heavy metals, and C. repens extract has good antioxidant capacity. Its antioxidant activity is realized by a variety of active factors through a synergistic mechanism. Thus, C. repens extract has great potential for developing into functional foods.
基金funded by the National Natural Science Foundation of China under Grant No.52002284the Young Elite Scientists Sponsorship Program by CAST under Grant No.2021QNRC001+1 种基金the Project funded by China Postdoctoral Science Foundation under Grant No.2021M692424the Jiangsu Province Science and Technology Project under Grant No.BE2021006-3.
文摘The type of road surface condition(RSC)will directly affect the driving performance of vehicles.Monitoring the type of RSC is essential for both transportation agencies and individual drivers.However,most existing methods are solely based on a dynamics-based method or an image-based method,which is susceptible to road excitation limitations and interference from the external environment.Therefore,this paper proposes a decision-level fusion identification framework of the RSC based on ego-vehicle trajectory reckoning to accurately obtain the type of RSC that the front wheels of the vehicle will expe-rience.First,a road feature extraction model based on multi-task learning is conducted,which can simultaneously segment the drivable area and road cast shadow.Second,the optimized candidate regions of interest are classified with confidence levels by ShuffleNet.Considering environmental interference,candidate regions of interest regarded as virtual sensors are fused by improved Dempster-Shafer evidence theory to obtain the fusion results.Finally,the ego-vehicle trajectory reckoning module based on the kinematic bicycle model is added to the proposed fusion method to extract the RSC experienced by the front wheels.The performance of the entire framework is verified on a specific dataset with shadow and split curve roads.The results reveal that the proposed method can identify the RSC with accurate predictions in real time.
基金This paper was supported by the National Natural Science Foundation of China under Grant 52002284the Shanghai Municipal Science and Technology Major Project under Grant 2021SHZDZX0100 and the Fundamental Research Funds for the Central Universities.
文摘Vehicle mass is an important parameter for motion control of intelligent vehicles,but is hard to directly measure using normal sensors.Therefore,accurate estimation of vehicle mass becomes crucial.In this paper,a vehicle mass estimation method based on fusion of machine learning and vehicle dynamic model is introduced.In machine learning method,a feedforward neural network(FFNN)is used to learn the relationship between vehicle mass and other state parameters,namely longitudinal speed and acceleration,driving or braking torque,and wheel angular speed.In dynamics-based method,recursive least square(RLS)with forgetting factor based on vehicle dynamic model is used to estimate the vehicle mass.According to the reliability of each method under different conditions,these two methods are fused using fuzzy logic.Simulation tests under New European Driving Cycle(NEDC)condition are carried out.The simulation results show that the estimation accuracy of the fusion method is around 97%,and that the fusion method performs better stability and robustness compared with each single method.
