Author ORCID Identifier
Date of Graduation
Statler College of Engineering and Mineral Resources
Lane Department of Computer Science and Electrical Engineering
The primary contribution is the development of the data collection testing methodology for autonomous driving systems of a hybrid electric passenger vehicle. As automotive manufacturers begin to develop adaptive cruise control technology in vehicles, progress is being made toward the development of fully-autonomous vehicles. Adaptive cruise control capability is classified into five levels defined by the Society of Automotive Engineering. Some vehicles under development have attained higher levels of autonomy, but the focus of most commercial development is Level 2 autonomy. As the level of autonomy increases, the sensor technology becomes more advanced with a sensor suite which includes radar, camera, and vehicle-to-everything radio. Sensors must detect the objects around the vehicle to be able for communicate the data to the adaptive cruise control algorithm. If a vehicle is in an accident, the driver is typically responsible for the damages, but with an autonomous vehicle, there might not be a driver. A process to guarantee a vehicle will perform as it was developed is critical to a vehicle’s development and testing. The goal of this work is to implement a verification and validation system that can be used on adaptive cruise control systems. The system developed in this paper used different testing environments such as model-in-the-loop, hardware-in-the-loop, and vehicle-in-the-loop, to fully validate an autonomous vehicle. A systematic data acquisition process has been developed to support autonomous vehicle development. The data that was taken had an organized way of comparing the results in each environment. Requirements management, vehicle logbook, and test case creation played a vital role in combining the information across the environments. The method produced a consumer-ready adaptive cruise control system in a 2019 Chevrolet Blazer RS. The vehicle was able to perform at an Advanced Vehicle Technology Competition where the adaptive cruise control system placed 1st in Connected and Automated Vehicle Perception System & Adaptive Cruise Control Drive Quality Evaluation. Results are presented that illustrate the utility of the data acquisition and multi-layer testing process for autonomous vehicle development.
Vincent, Clay Edward, "Framework for Data Acquisition and Fusion of Camera and Radar for Autonomous Vehicle Systems" (2023). Graduate Theses, Dissertations, and Problem Reports. 12102.