Connected and Automated Vehicles (CAVs) have the potential to greatly improve roadway safety, mobility, and associated environmental factors. 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 localization, which can be significant given CAVs’ strong reliance on Global Navigation Satellite Systems (GNSS)-based systems for positioning. Positioning accuracy can vary both in time and space and is sometimes quite poor (>10 meters) in urban and other challenging environments for GNSS. This is a problem given that many CAV applications (e.g., Cooperative Adaptive Cruise Control) require at least lane-level, or submeter, accuracy.
This dissertation focuses on the gap between current (affordable) Connected Vehicle positioning technology and CAV applications’ positioning requirements. As a first step, the positioning requirements of a wide range of CAV applications were qualitatively determined. The positioning attributes examined were: required accuracy, type (i.e., relative vs. absolute positioning), and update rate; and the “maximum benefit” accuracy. It was found that lane-level positioning accuracy is critical to enabling functionality in a large number of applications.
Next, the effect of position uncertainty on application performance was tested in a simulation environment. Two applications—an environmentally-friendly application for signalized intersections, and a safety-focused highway application—were examined. In both cases, it was found that application performance can degrade significantly (or outright fail) when the positioning accuracy is less than required.
Given the negative effect of position uncertainty on application performance, a position error-tolerant (PE-T) application was developed. A CAV application, (cooperative merging) was developed and modified so that it could function when position errors typical of an urban environment were present. In addition, it was found that adjusting the application in response to position error (by increasing inter-vehicle spacing) could increase application benefits in some scenarios. Another way in which this application is novel compared to most other existing cooperative merging strategies is that it can accommodate application penetration rates varying from 0% to 100%. In fact, significant safety