The existing electronic stability control(ESC)system does not consider the steering intention of an autonomous driving system(ADS)during autonomous driving emergency avoidance.The distributed control scheme between ES...The existing electronic stability control(ESC)system does not consider the steering intention of an autonomous driving system(ADS)during autonomous driving emergency avoidance.The distributed control scheme between ESC and ADS does not involve any information interaction,and the balance between path tracking and lateral stability is not optimal.A Nash game control scheme of steering and braking system is proposed to ensure that both path tracking and stability are simultaneously in the loop and solve the conflict by simulating the interaction between the two agents considering the impact on the interaction between a path following and lateral stability.Each agent considers its interests and the opponent’s interest,resulting in a global optimal control solution of steering control and ESC.Then,using minimax optimization theory,a lateral stability strategy is proposed to make the game control architecture under emergency avoidance more robust to uncertain lateral disturbances.Finally,simulation and hardware in the loop experiments show that the proposed scheme can consider both vehicle’s path following performance and lateral stability in the event of an emergency.展开更多
The conventional phase plane method estimates the stability of vehicle systems using fixed inputs.This approach exhibits a clear inclination towards conservatism,potentially leading to unnecessary interference with th...The conventional phase plane method estimates the stability of vehicle systems using fixed inputs.This approach exhibits a clear inclination towards conservatism,potentially leading to unnecessary interference with the driver or failures in collision avoidance due to stringent limitations during emergency scenarios.To address the aforementioned issues,this study introduces a regression region constraint by extending the phase plane method.Subsequently,an emergency collision avoidance system(ECAS)is developed based on this regression region.According to the emergency degree of the scenario,ECAS system is divided into 2 modes:the immediate takeover mode and the risk monitoring mode.In the mode of immediate takeover,the system promptly substitutes the driver upon encountering obstacles and employs the collision avoidance algorithm based on optimal control and model predictive control to avoid collisions.When the vehicle states exceed the constraint of the regression region,the regression stability algorithm is triggered to make the vehicle regress to stability.In the risk monitoring mode,the driver avoids collision by himself,and the system monitors the vehicle states.The regression stability algorithm replaces the driver once the regression region constraint is reached.The performance of two intervention modes was validated based on the dSPACE HIL platform and G29 driving simulator correspondingly.The results suggest that the immediate takeover mode can be qualified for collision avoidance conditions with the longitudinal distance of 33.0 m at the speed of 30 m/s,while the shortest distance the existing studies can deal with is 37.5 m,and the risk monitoring mode can also successfully avoid unnecessary interference when the driver is competent.展开更多
The behaviors of front vehicles are important factors that can influence the driving safety of autonomous vehicles on highways.This situation poses a serious threat to the security of autonomous vehicles,especially wh...The behaviors of front vehicles are important factors that can influence the driving safety of autonomous vehicles on highways.This situation poses a serious threat to the security of autonomous vehicles,especially when front vehicle sideslip occurs.To address this problem,a decision-making approach can be used to promote the emergency obstacle avoidance capability of autonomous vehicles.First,the front sideslip vehicle trajectory was predicted by the kinematic models Constant Acceleration(CA),Constant Turn Rate and Velocity(CTRV),and Constant Turn Rate and Acceleration(CTRA)based on the front vehicle sideslip identification results.The CTRA prediction approach is chosen by comparing the prediction errors of the three models.To enhance the obstacle avoidance ability of autonomous vehicles,a novel trajectory planning method based on a driving characteristic vector is proposed.Model prediction control(MPC)is used to track the planned trajectory.Finally,the cosimulation platform of Simulink and Carsim was built.The simulation results show that autonomous vehicles can avoid collisions with front sideslip vehicles through the proposed approach,and the proposed trajectory planning approach has better obstacle avoidance ability than does the traditional approach.展开更多
基金supported by the National Natural Science Foundation of China (Grant No. 52072072)Shandong Province Major Scientific and Technological Innovation Projects (Grant No. 2019TSLH0701)
文摘The existing electronic stability control(ESC)system does not consider the steering intention of an autonomous driving system(ADS)during autonomous driving emergency avoidance.The distributed control scheme between ESC and ADS does not involve any information interaction,and the balance between path tracking and lateral stability is not optimal.A Nash game control scheme of steering and braking system is proposed to ensure that both path tracking and stability are simultaneously in the loop and solve the conflict by simulating the interaction between the two agents considering the impact on the interaction between a path following and lateral stability.Each agent considers its interests and the opponent’s interest,resulting in a global optimal control solution of steering control and ESC.Then,using minimax optimization theory,a lateral stability strategy is proposed to make the game control architecture under emergency avoidance more robust to uncertain lateral disturbances.Finally,simulation and hardware in the loop experiments show that the proposed scheme can consider both vehicle’s path following performance and lateral stability in the event of an emergency.
基金supported jointly by the National Natural Science Foundation of China(51875184,52202466,52262054)the Natural Science Foundation of Fujian Province(2023J01245).
文摘The conventional phase plane method estimates the stability of vehicle systems using fixed inputs.This approach exhibits a clear inclination towards conservatism,potentially leading to unnecessary interference with the driver or failures in collision avoidance due to stringent limitations during emergency scenarios.To address the aforementioned issues,this study introduces a regression region constraint by extending the phase plane method.Subsequently,an emergency collision avoidance system(ECAS)is developed based on this regression region.According to the emergency degree of the scenario,ECAS system is divided into 2 modes:the immediate takeover mode and the risk monitoring mode.In the mode of immediate takeover,the system promptly substitutes the driver upon encountering obstacles and employs the collision avoidance algorithm based on optimal control and model predictive control to avoid collisions.When the vehicle states exceed the constraint of the regression region,the regression stability algorithm is triggered to make the vehicle regress to stability.In the risk monitoring mode,the driver avoids collision by himself,and the system monitors the vehicle states.The regression stability algorithm replaces the driver once the regression region constraint is reached.The performance of two intervention modes was validated based on the dSPACE HIL platform and G29 driving simulator correspondingly.The results suggest that the immediate takeover mode can be qualified for collision avoidance conditions with the longitudinal distance of 33.0 m at the speed of 30 m/s,while the shortest distance the existing studies can deal with is 37.5 m,and the risk monitoring mode can also successfully avoid unnecessary interference when the driver is competent.
基金supported by the National Key R&D Program of China(Grant No.2022YFE0101000).
文摘The behaviors of front vehicles are important factors that can influence the driving safety of autonomous vehicles on highways.This situation poses a serious threat to the security of autonomous vehicles,especially when front vehicle sideslip occurs.To address this problem,a decision-making approach can be used to promote the emergency obstacle avoidance capability of autonomous vehicles.First,the front sideslip vehicle trajectory was predicted by the kinematic models Constant Acceleration(CA),Constant Turn Rate and Velocity(CTRV),and Constant Turn Rate and Acceleration(CTRA)based on the front vehicle sideslip identification results.The CTRA prediction approach is chosen by comparing the prediction errors of the three models.To enhance the obstacle avoidance ability of autonomous vehicles,a novel trajectory planning method based on a driving characteristic vector is proposed.Model prediction control(MPC)is used to track the planned trajectory.Finally,the cosimulation platform of Simulink and Carsim was built.The simulation results show that autonomous vehicles can avoid collisions with front sideslip vehicles through the proposed approach,and the proposed trajectory planning approach has better obstacle avoidance ability than does the traditional approach.
