Autonomous Vehicle Lateral Maneuvering by Approximate Explicit Predictive Control

초록

In this paper, we propose a lateral control (LC) method of an autonomous vehicle using an efficient model predictive control (MPC). Although model predictive control is effective in solving vehicle LC problems, it is difficult for a standard MPC to be applied to the lateral motion control of a vehicle due to its computational time required less than 10 [ms] for chassis control. Our proposed approximate explicit model predictive control (AEMPC) scheme does not require online quadratic programming but it uses pre-computed control gains. Therefore, it is suitable for chassis control requiring computation time less than a few msec. The proposed AEMPC is so simple and systematic that it can be implemented in solving the MPC problem in real-time with the digital signal processor of an electronic control unit (ECU) used for automotive applications. we validated how useful the proposed AEMPC is when applied to LC through computational experiment.