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The demand for smart control technology is glowing according to the increase of expectation for autonomous driving. This paper presents an integrated planning and control scheme for automated lane-change behavior based on real-time nonlinear model predictive control (NMPC). In the proposed method, switched weighting parameters are used in the cost function over the prediction horizon. This enables us to reduce the number of reference points for control compared with the conventional framework. In addition, some safety constraints, such as collision avoidance and/or speed limitation are considered in a consistent manner. Although the NMPC usually requires huge computational cost, by applying the Continuation/GMRES (C/GMRES) method, it can be drastically reduced and become possible to be implemented in real time. Finally, validity of the proposed method is demonstrated by simulation study.

This paper presents a control system for autonomous driving based on MPC in which driving style can be easily modified by changing control parameters. Each of the motion controls for the longitudinal and the lateral direction are formulated as the model predictive control problem. Finally the experimental verification by using driving simulator and a real electric vehicle is performed by implementing MPC on each platform, and it is confirmed that the proposed system can produce a large variety of driving characteristics. The implemented MPC package will also be beneficial to the developers and researchers in various fields other than control engineering field.

This paper presents an autonomous lane tracking that reflects different driving characteristics using model predictive control (MPC). We consider that human driver minimizes a cost function depending on his/her skill, experience, and preference on driving. The cost function of MPC can be used to model personal driving characteristics. To identify the parameters of cost function, at first, we analyze the difference of driving characteristics between skilled and un-skilled drivers through experiments of driving behavior on a driving simulator (DS). Next, we introduce "meta-performance indices" that can evaluate human driving data of experiment and results of autonomous driving. These parameters are expected to express the driving characteristics as a group, i.e., not tuned to personal driver. Finally, the validity of the proposed system is verified.