The Department of Mechanical Engineering presents:
The Ph.D. Dissertation Defense of
Nigel Williams

Friday, November 15, 2019,
2PM in Winston Chung Hall 205/206

Integrating Position Uncertainty in
Connected and Automated Vehicle Applications

Doctor of Philosophy, Graduate Program in Mechanical Engineering
University of California, Riverside, November 2019
Dr. Matthew J. Barth, Chairperson

Connected and Automated Vehicles (CAVs) have the potential to greatly improve roadway safety, mobility, and environmental impact. To date, a large number of CAV applications have been developed and tested, both in the real world and in simulation. In nearly all cases, it is assumed that vehicle localization is sufficiently accurate at all times. Only a few studies have accounted for uncertainty in vehicle position measurements, which can be significant given CAVs’ reliance on Global Navigation Satellite Systems (GNSS) -based systems for positioning. Positioning accuracy can vary both in time and space and are sometimes quite large (>10 meters) in urban and other challenging environments for GNSS. A wide range of CAV applications were surveyed, and it was found that lane-level (submeter) position accuracy is critical to enabling functionality in a large number of applications. It was also found that application performance can degrade significantly when substandard positioning is used. Finally, a CAV application (Cooperative Merging) was developed and tested extensively in simulation. It was shown that the application could be adjusted to reduce some of the negative impact of positioning error.