This paper presents an FBCRI(feedback based compositional rule of inference)based novel path planning method to satisfy the requirements of real-time navigation,smoothness optimization and probabilistic obstacle avoid...This paper presents an FBCRI(feedback based compositional rule of inference)based novel path planning method to satisfy the requirements of real-time navigation,smoothness optimization and probabilistic obstacle avoidance.With local path-searching behaviors in regional ranges and global goal-seeking behaviors in holistic ranges,the method infers behavior weights using fuzzy reasoning embedded with feedback,and then coordinates the behaviors to generate new reference waypoints.In view of the deterministic decisions and the uncertain states of a UAV(unmanned air vehicle),chance constraints are adopted to probabilistically guarantee the UAV’s safety at a required level.Simulation results in representative scenes prove that the method is able to rapidly generate convergent paths in obstacle-rich environments,as well as highly improve the path quality with respect to smoothness and probabilistic safety.展开更多
Purpose–This study aims to develop an automatic lane-change mechanism on highways for self-driving articulated trucks to improve traffic safety.Design/methodology/approach–The authors proposed a novel safety lane-cha...Purpose–This study aims to develop an automatic lane-change mechanism on highways for self-driving articulated trucks to improve traffic safety.Design/methodology/approach–The authors proposed a novel safety lane-change path planning and tracking control method for articulated vehicles.A double-Gaussian distribution was introduced to deduce the lane-change trajectories of tractor and trailer coupling characteristics of intelligent vehicles and roads.With different steering and braking maneuvers,minimum safe distances were modeled and calculated.Considering safety and ergonomics,the authors invested multilevel self-driving modes that serve as the basis of decision-making for vehicle lane-change.Furthermore,a combined controller was designed by feedback linearization and single-point preview optimization to ensure the path tracking and robust stability.Specialized hardware in the loop simulation platform was built to verify the effectiveness of the designed method.Findings–The numerical simulation results demonstrated the path-planning model feasibility and controller-combined decision mechanism effectiveness to self-driving trucks.The proposed trajectory model could provide safety lane-change path planning,and the designed controller could ensure good tracking and robust stability for the closed-loop nonlinear system.Originality/value–This is a fundamental research of intelligent local path planning and automatic control for articulated vehicles.There are two main contributions:thefirst is a more quantifiable trajectory model for self-driving articulated vehicles,which provides the opportunity to adapt vehicle and scene changes.The second involves designing a feedback linearization controller,combined with a multi-objective decision-making mode,to improve the comprehensive performance of intelligent vehicles.This study provides a valuable reference to develop advanced driving assistant system and intelligent control systems for self-driving articulated vehicles.展开更多
基金National Nature Science Foundation of China(60904066)
文摘This paper presents an FBCRI(feedback based compositional rule of inference)based novel path planning method to satisfy the requirements of real-time navigation,smoothness optimization and probabilistic obstacle avoidance.With local path-searching behaviors in regional ranges and global goal-seeking behaviors in holistic ranges,the method infers behavior weights using fuzzy reasoning embedded with feedback,and then coordinates the behaviors to generate new reference waypoints.In view of the deterministic decisions and the uncertain states of a UAV(unmanned air vehicle),chance constraints are adopted to probabilistically guarantee the UAV’s safety at a required level.Simulation results in representative scenes prove that the method is able to rapidly generate convergent paths in obstacle-rich environments,as well as highly improve the path quality with respect to smoothness and probabilistic safety.
文摘Purpose–This study aims to develop an automatic lane-change mechanism on highways for self-driving articulated trucks to improve traffic safety.Design/methodology/approach–The authors proposed a novel safety lane-change path planning and tracking control method for articulated vehicles.A double-Gaussian distribution was introduced to deduce the lane-change trajectories of tractor and trailer coupling characteristics of intelligent vehicles and roads.With different steering and braking maneuvers,minimum safe distances were modeled and calculated.Considering safety and ergonomics,the authors invested multilevel self-driving modes that serve as the basis of decision-making for vehicle lane-change.Furthermore,a combined controller was designed by feedback linearization and single-point preview optimization to ensure the path tracking and robust stability.Specialized hardware in the loop simulation platform was built to verify the effectiveness of the designed method.Findings–The numerical simulation results demonstrated the path-planning model feasibility and controller-combined decision mechanism effectiveness to self-driving trucks.The proposed trajectory model could provide safety lane-change path planning,and the designed controller could ensure good tracking and robust stability for the closed-loop nonlinear system.Originality/value–This is a fundamental research of intelligent local path planning and automatic control for articulated vehicles.There are two main contributions:thefirst is a more quantifiable trajectory model for self-driving articulated vehicles,which provides the opportunity to adapt vehicle and scene changes.The second involves designing a feedback linearization controller,combined with a multi-objective decision-making mode,to improve the comprehensive performance of intelligent vehicles.This study provides a valuable reference to develop advanced driving assistant system and intelligent control systems for self-driving articulated vehicles.
