Intelligent vehicles can utilize a variety of sensors, computing, and control
technologies to autonomously perceive the environment and make decisions to
achieve safe, efficient, and automated driving. If the speed planning of
intelligent vehicles ignores the vehicle dynamics state, it leads to
unreasonable planning speed and is not conducive to improving the accuracy of
trajectory tracking control. Meanwhile, trajectory tracking usually does not
consider the road and speed information beyond the prediction horizon, resulting
in poor tracking precision that is not conducive to improving driving comfort.
To solve these problems, this study proposes a new longitudinal speed planning
method based on variable universe fuzzy rules and designs the piecewise preview
model predictive control (PPMPC) to realize the vehicle trajectory tracking.
First, the three-degrees-of-freedom vehicle dynamics model and trajectory
tracking model are established and verified. Then, the variable universe fuzzy
rules are introduced to design the longitudinal speed planning method, in which
the road friction coefficient and road curvature are defined as the input of the
speed planning method, and the vehicle lateral deviation is defined as the
scaling factor input of the speed variable universe. Based on the dynamics model
and trajectory tracking model, the PPMPC method is proposed to improve the
accuracy and stability of trajectory tracking. During the PPMPC method design,
the reference value of state quantity in the prediction horizon can be updated
by using further road information and planning longitudinal speed information.
Finally, the results show that the proposed planning algorithm can provide a
reasonable longitudinal speed to reduce the tracking lateral error in the
tracking control, and the proposed PPMPC can significantly improve the vehicle
speed-tracking accuracy and control stability compared with the traditional
model predictive control (MPC) method.
