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In this paper, an autonomous driving lateral control algorithm is introduced for bus rapid transit (BRT, with an articulated bus). The control algorithm includes an integration of model predictive control (MPC) for lateral control and PID control algorithms for longitudinal control considering the vehicle properties. To verify the algorithm in real-time, a model-in-the-loop system was developed using TruckSim in NI Veristand and Matlab/Simulink in Micro-AutoBox. In TruckSim, a BRT plant model was verified using vehicle tests and then applied. In addition, GPS data from a BRT route were acquired from the vehicle test and applied to a TruckSim scenario. In the Matlab/Simulink, the autonomous driving lateral control algorithm for MPC was developed. To communicate between the plant model and the control algorithm in real-time, a controller area network (CAN) protocol was defined and applied like a real vehicle. Therefore, a real-time verification environment was prepared to test the real-time autonomous driving lateral control algorithm for BRT. By using the verification environment, the control algorithm was verified using the ISO-11270 standard. Although many efforts have been made to develop an autonomous driving control algorithm, autonomous driving of BRT is expected more tangible because the BRT has its own lane and speed limitation. Therefore, this paper will introduce about study of real time feasibility, adaptation of vehicle variables, and precise prediction model for MPC algorithm. [ABSTRACT FROM AUTHOR]

This research suggests an autonomous driving control algorithm for Bus Rapid Transit (BRT, articulated bus). The control algorithm is composed of an integration of MPC (model Predictive Control) and PI control algorithms because vehicles have a lot of delayed systems. Specifically, an MPC algorithm is used as a lateral controller, and a PI control algorithm is used as a longitudinal controller in parallel. To develop the MPC algorithm, path information (path planning) and an inner model is necessary. Thus, the path information is defined as the road center since the BRT is traveling a certain path, and the inner model is defined from the bicycle model of vehicle dynamics. The wheel steering input is controlled based on the predicted lateral movement using the inner model. To develop the PI algorithm, brake pressure and throttle position inputs are controlled based on the longitudinal velocity error. As a result, the integrated control algorithm of MPC and PI is developed in commercial tool Matlab/Simulink with a plant model which is developed in commercial tool TruckSim. In conclusion, the integrated control algorithm is verified in the simulation environment with a standard of ISO-11270 which is to test a Lane Keeping Assist System (LKAS). [ABSTRACT FROM AUTHOR]