Development and Implementation of an Adaptive Cruise Control Feature in a Consumer Vehicle
Abstract
This thesis covers work to develop and implement an Adaptive Cruise Control (ACC) feature in a consumer vehicle. More specifically, the development of a perception system and vehicle controller enabling automated control over a vehicle’s longitudinal motion will be discussed. The ACC feature is able to follow other vehicles while maintaining a safe following distance or to maintain a user-set velocity in the absence of a lead vehicle. This research is driven by a larger competition, the EcoCAR Mobility Challenge, sponsored by the U.S. Department of Energy, General Motors (GM), and Mathworks. The competition tasks teams with integrating a hybrid drivetrain and developing Connected and Autonomous Vehicle (CAVs) technology to improve energy efficiency, rider comfort, and safety in a 2019 Chevy Blazer. This research leveraged off-the-shelf industry grade tools, sensors, and actuators including MATLAB’s Automated Driving and Sensor Fusion and Tracking Toolboxes, the Robot Operating System (ROS), a Mobileye camera unit, a Bosch radar sensor, and electric machines from Denso and Magna. The problems were modeled in a software environment and then deployed in the road vehicle to evaluate and tune performance. These features will be evaluated by the competition in a final event in May 2022 at a GM proving grounds facility.