Access Type

Open Access Embargo

Date of Award

January 2019

Degree Type


Degree Name



Electrical and Computer Engineering

First Advisor

Caisheng Wang

Second Advisor

Chin-An Tan


Advanced Driver Assistance Systems (ADAS) have been developed in recent years to significantly improve safety in driving and assist driver’s response in extreme situations in which quick decisions and maneuvers are required. Common features of ADAS in modern vehicles include automatic emergency braking (AEB), lane keeping assistance (LKA), electric stability control (ESC), and adaptive cruise control (ACC). While these features are developed primarily based on sensor fusion, image processing and vehicle kinematics, the importance of vehicle dynamics must not be overlooked to ensure that the vehicle can follow the desired trajectory without inducing any instability. In many extreme situations such as object avoidance, fast maneuvering of vehicles with high center of gravity might result in rollover instability, an event with a high fatality rate. It is thus necessary to incorporate vehicle dynamics into ADAS to improve the robustness of the system in the path planning to avoid collision with other vehicles or objects and prevent vehicle instability. The objectives of this thesis are to examine the efficacy of a vehicle dynamics model in ADAS to simulate rollover and to develop an active controller using Model Predictive Control (MPC) to manipulate the front-wheel steering and four-wheel differential braking forces, which are related to active steering as well as dynamic stability control for collision avoidance. The controller is designed using the model predictive control approach. A four degree-of-freedom vehicle model is simulated and tested in various scenarios. According to simulation results, the vehicle controller by the MPC controller can track the predicted path within error tolerance. The trajectories used in different simulation scenarios are generated by the MPC controller.

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