TY - GEN
T1 - Approximated long horizon MPC with hindsight for autonomous vehicles path tracking
AU - Jiang, Chaoyang
AU - Zhai, Jiankun
AU - Tian, Hanqing
AU - Wei, Chao
AU - Hu, Jibin
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/27
Y1 - 2020/11/27
N2 - We propose an approximated long horizon model predictive control (MPC) for path tracking of autonomous vehicles, which is more computationally efficient than a standard MPC with a long horizon and more effective than a standard MPC with a short horizon. In the proposed MPC, the cost function consists of two parts: 1) the cost function of the short horizon MPC, and 2) an additional term to approximate the difference between the cost function with the short horizon and that with the long horizon, which we call the hindsight cost function. The additional term is obtained from a linear regression model that is offline learned from previous known trajectory data. Finally, a CarSim-MATLAB/Simulink co-simulation is provided to show the effectiveness of the proposed approximated long horizon MPC.
AB - We propose an approximated long horizon model predictive control (MPC) for path tracking of autonomous vehicles, which is more computationally efficient than a standard MPC with a long horizon and more effective than a standard MPC with a short horizon. In the proposed MPC, the cost function consists of two parts: 1) the cost function of the short horizon MPC, and 2) an additional term to approximate the difference between the cost function with the short horizon and that with the long horizon, which we call the hindsight cost function. The additional term is obtained from a linear regression model that is offline learned from previous known trajectory data. Finally, a CarSim-MATLAB/Simulink co-simulation is provided to show the effectiveness of the proposed approximated long horizon MPC.
KW - Approximated long horizon MPC
KW - Hindsight cost function
KW - Linear regression
KW - Path tracking
UR - http://www.scopus.com/inward/record.url?scp=85098984834&partnerID=8YFLogxK
U2 - 10.1109/ICUS50048.2020.9274850
DO - 10.1109/ICUS50048.2020.9274850
M3 - Conference contribution
AN - SCOPUS:85098984834
T3 - Proceedings of 2020 3rd International Conference on Unmanned Systems, ICUS 2020
SP - 696
EP - 701
BT - Proceedings of 2020 3rd International Conference on Unmanned Systems, ICUS 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Unmanned Systems, ICUS 2020
Y2 - 27 November 2020 through 28 November 2020
ER -