Stability-Considered Lane Keeping Control of Commercial Vehicles Based on Improved APF Algorithm

Regarding the lane keeping system,path tracking accuracy and lateral stability at high speeds need to be taken into account especially for commercial vehicles due to the characteristics of larger mass,longer wheelbase and higher mass center.To improve the performance mentioned above comprehensively,the control strategy based on improved artificial potential field(APF)algorithm is proposed.In the paper,time to lane crossing(TLC)is introduced into the potential field function to enhance the accuracy of path tracking,meanwhile the vehicle dynamics parameters including yaw rate and lateral acceleration are chosen as the repulsive force field source.The lane keeping controller based on improved APF algorithm is designed and the stability of the control system is proved based on Lyapunov theory.In addition,adaptive inertial weight particle swarm optimization algorithm(AIWPSO)is applied to optimize the gain of each potential field function.The co-simulation results indicate that the comprehensive evaluation index respecting lane tracking accuracy and lateral stability is reduced remarkably.Finally,the proposed control strategy is verified by the HiL test.It provides a beneficial reference for dynamics control of commercial vehicles and enriches the theoretical development and practical application of artificial potential field method in the field of intelligent driving.

Stochastic Model Predictive Control Approach to Autonomous Vehicle Lane Keeping

In real-world scenarios,the uncertainty of measurements cannot be handled efficiently by traditional model predictive control(MPC).A stochastic MPC(SMPC)method for handling the uncertainty of states in autonomous driving lane-keeping scenarios is presented in this paper.A probabilistic system is constructed by considering the variance of states.The probabilistic problem is then transformed into a solvable deterministic optimization problem in two steps.First,the cost function is separated into mean and variance components.The mean component is calculated online,whereas the variance component can be calculated offline.Second,Cantelli’s inequality is adopted for the deterministic reformulation of constraints.Consequently,the original probabilistic problem is transformed into a quadratic programming problem.To validate the feasibility and effectiveness of the proposed control method,we compared the SMPC controller with a traditional MPC controller in a lane-keeping scenario.The results demonstrate that the SMPC controller is more effective overall and produces smaller steady-state distance errors.