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
This paper designs a novel controller to improve the path-tracking performance of articulated dump truck(ADT). By combining linear quadratic regulator(LQR) with genetic algorithm(GA), the designed controller is used t...This paper designs a novel controller to improve the path-tracking performance of articulated dump truck(ADT). By combining linear quadratic regulator(LQR) with genetic algorithm(GA), the designed controller is used to control linear and angular velocities on the midpoint of the front frame. The novel controller based on the error dynamics model is eventually realized to track the path high-precisely with constant speed. The results of simulation and experiment show that the LQR-GA controller has a better tracking performance than the existing methods under a low speed of 3 m/s. In this paper, kinematics model and simulation control models based on co-simulation of ADAMS and Matlab/Simulink are established to verify the proposed strategy. In addition, a real vehicle experiment is designed to further more correctness of the conclusion. With the proposed controller and considering the steering model in the simulation, the control performance is improved and matches the actual situation better. The research results contribute to the development of automation of ADT.展开更多
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.
基金the Fundamental Research Funds for the Central Universities of China(No.FRF-TP-15-023A1)the National Key R&D Program Project(Nos.2016YFC0802905 and 2018YFC0604403)
文摘This paper designs a novel controller to improve the path-tracking performance of articulated dump truck(ADT). By combining linear quadratic regulator(LQR) with genetic algorithm(GA), the designed controller is used to control linear and angular velocities on the midpoint of the front frame. The novel controller based on the error dynamics model is eventually realized to track the path high-precisely with constant speed. The results of simulation and experiment show that the LQR-GA controller has a better tracking performance than the existing methods under a low speed of 3 m/s. In this paper, kinematics model and simulation control models based on co-simulation of ADAMS and Matlab/Simulink are established to verify the proposed strategy. In addition, a real vehicle experiment is designed to further more correctness of the conclusion. With the proposed controller and considering the steering model in the simulation, the control performance is improved and matches the actual situation better. The research results contribute to the development of automation of ADT.
