Abstract
Autonomous driving is a breakthrough technology in the automobile and transportation fields. The characteristics of planned trajectories and tracking accuracy affect the development of autonomous driving technology. To improve the measurement accuracy of the vehicle state and realise the online application of predictive control algorithm, an online active set-based longitudinal and lateral model predictive tracking control method of autonomous driving is proposed for electric vehicles. Integrated with the vehicle inertial measurement unit (IMU) and global positioning system (GPS) information, a vehicle state estimator is designed based on an extended Kalman filter. Based on the 3-degree-of-freedom vehicle dynamics model and the curvilinear road coordinate system, the longitudinal and lateral errors dimensionality reduction is carried out. A fast-rolling optimisation algorithm for longitudinal and lateral tracking control of autonomous vehicles is designed and implemented based on convex optimisation, online active set theory and QP solver. Finally, the performance of the proposed tracking control method is verified in the reconstructed curve road scene based on real GPS data. The hardware-in-the-loop simulation results show that the proposed MPC controller has apparent advantages compared with the PID-based controller.
Highlights
Due to the advantages in energy and environment, electric vehicles have become the most popular research area
(2) According to the longitudinal and lateral multi-target tracking control requirements of the autonomous vehicle, the longitudinal and lateral error dimensionality reduction in path tracking is designed based on the 3DOF vehicle dynamics model and curved road coordinate system
In the path tracking control with the target tracking speed set to 60 km/h, the reference trajectory results of the PID controller and the model predictive control (MPC) controller are shown in Figure 6a,b, respectively
Summary
Due to the advantages in energy and environment, electric vehicles have become the most popular research area. For the problem of multi constraint processing and multi-target tracking in autonomous vehicle tracking control, model predictive control (MPC) based on optimal control theory is undoubtedly one of the most widely studied and applied control algorithms In this regard, Song et al [12] proposed longitudinal and lateral control strategies based on MPC and realised the expected speed and expected path following. To improve the measurement accuracy of the vehicle state and realise the online application of predictive control algorithm, an online active setbased longitudinal and lateral model predictive tracking control of autonomous driving is proposed in this paper. (2) According to the longitudinal and lateral multi-target tracking control requirements of the autonomous vehicle, the longitudinal and lateral error dimensionality reduction in path tracking is designed based on the 3DOF vehicle dynamics model and curved road coordinate system.
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