Research On Trajectory Planning And Control Method Of Unmanned Vehicles In Urban Road Scenarios

In recent years,with the development of information technology and the support of industrial transformation,the automotive industry has been gradually moving towards unmanned and intelligent direction.Unmanned driving technology is a vehicle intelligence technology that integrates high-definition map,environment perception,behavior decision,trajectory planning,and tracking control.Among them,trajectory planning and tracking control are the two key technologies in unmanned driving,which directly affect the vehicle’s driving safety level,traffic efficiency,and vehicle stability.This thesis takes the urban road scenario as the background,decouples the threedimensional trajectory planning problem of vehicles into two two-dimensional planning problems containing lateral and longitudinal directions based on the Frenet coordinate system and trajectory decoupling ideas.A local trajectory planning driving risk field model based on the improved two-dimensional Gaussian probability distribution function is established,and the established risk field model is introduced into the trajectory planning process,proposing an unmanned driving vehicle trajectory planning method based on the optimized sampling area.A linear time-varying model predictive controller based on the vehicle dynamics model is designed.The main research contents of this thesis are as follows:(1)In the trajectory planning part,this thesis proposes a trajectory planning method based on the risk field model for sampling area optimization.Firstly,the trajectory is decoupled into lateral and longitudinal dimensions based on Frenet coordinate system,then the road centerline prior information is fully utilized,and an adaptive grid method is introduced for lateral and longitudinal sampling of the road for generating lateral and longitudinal trajectories of autonomous vehicles.Finally,a loss cost function containing lateral cost function,longitudinal cost function and collision risk assessment cost function is constructed for trajectory quality assessment,and the trajectory that minimizes the cost function is output as the trajectory planning result.To further improve the real-time performance and efficiency of the algorithm,a local trajectory planning driving risk field model for unmanned vehicles is established based on an improved two-dimensional Gaussian distribution function,which unifies the quantification of driving risk in the road environment.The established risk field model is introduced into the trajectory planning process,and the driving risk of the sampling area target points is quantified by combining the risk field quantification principle.Low-risk area sampling target points are selected for trajectory generation and feasibility optimization through convolution.The simulation results show that the proposed trajectory optimization method can reduce the number of trajectories that need to be traversed by the planning algorithm,effectively improving the algorithm’s computational efficiency and vehicle driving efficiency,making the trajectory planning results more reasonable.(2)In the trajectory tracking control part,this thesis designs a linear time-varying model predictive controller(LTV-MPC)based on the three-degree-of-freedom dynamics model.Firstly,to ensure the smoothness of control,an incremental MPC controller is designed and the relevant theoretical derivation is completed.Secondly,the nonlinear model is linearized to ensure the solving efficiency,and an optimization objective function considering multiple constraints such as control quantity and control increment constraints,control output constraints,and vehicle stability constraints is designed.The optimization problem is transformed into a standard quadratic programming problem for convenient computer solving.Finally,simulation scenarios are built to verify the trajectory tracking performance of the algorithm.The simulation results show that the designed trajectory tracking controller can track the desired trajectory quickly and stably,and meet the accuracy requirements of trajectory tracking,and have stable control output changes without significant oscillation,demonstrating good control stability and adaptability.(3)In the algorithm joint simulation verification part,a joint simulation platform is built based on MATLAB/Simulink and Carsim.Starting from the requirements of algorithm testing,the feasibility verification and analysis of the planning and control algorithm designed in this thesis are carried out for several typical urban road scenarios.The simulation results show that the trajectory planning algorithm designed in this thesis can plan reasonable driving trajectories that meet certain constraints.The designed trajectory tracking controller can achieve smooth tracking of the preset trajectory with a relatively small tracking error,meeting the trajectory tracking requirements of autonomous vehicles.In summary,the planning and control algorithm designed in this thesis can meet the safety,comfort,and efficiency requirements of autonomous vehicles driving in urban road scenarios.