| Summary: | Indiana University-Purdue University Indianapolis (IUPUI) === Collision avoidance design plays an essential role in autonomous vehicle technology.
It's an attractive research area that will need much experimentation in the
future. This research area is very important for providing the maximum safety to automated vehicles, which have to be tested several times under diFFerent circumstances
for safety before use in real life.
This thesis proposes a method for designing and presenting a collision avoidance
maneuver by using a model predictive controller with a moving obstacle for automated
vehicles. It consists of a plant model, an adaptive MPC controller, and a reference
trajectory. The proposed strategy applies a dynamic bicycle model as the plant
model, adaptive model predictive controller for the lateral control, and a custom
reference trajectory for the scenario design. The model was developed using the
Model Predictive Control Toolbox and Automated Driving Toolbox in Matlab. Builtin
tools available in Matlab/Simulink were used to verify the modeling approach and
analyze the performance of the system.
The major contribution of this thesis work was implementing a novel dynamic
obstacle avoidance control method for automated vehicles. The study used validated
parameters obtained from previous research. The novelty of this research was performing
the studies using a MPC based controller instead of a sliding mode controller,
that was primarily used in other studies. The results obtained from the study are compared
with the validated models. The comparisons consisted of the lateral overlap,
lateral error, and steering angle simulation results between the models. Additionally,
this study also included outcomes for the yaw angle. The comparisons and other outcomes obtained in this study indicated that the developed control model produced
reasonably acceptable results and recommendations for future studies.
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