As an emerging solution to numerous socio-economic and environmental issues in our contemporary transportation systems, Connected and Automated Vehicles (CAVs) have received a significant amount of attention from industry, government, and academia. A variety of CAV-based applications have been developed to decrease the frequency and severity of accidents, mitigate congestion, reduce energy consumption and pollutant emissions, as well as enhance system resilience and efficiency. The vast majority of such applications require accurate and reliable vehicle localization.
This project will investigate and demonstrate the utility of lane-level localization accuracy and map-matching in a selected CAV application. The project aims to achieve four goals: (1) to investigate CAV applications to identify which have the potential to provide benefits in sustainable transportation from lane-level positioning and are suitable for implementation within the Riverside Innovation Corridor; (2) investigate, develop, and implement lane-level mapping; (3) investigate, develop, and implement lane-level map-matching; and (4) demonstration of lane-level map-matching in the selected application.
The project results will be disseminated publicly through published papers that describe methods for achieving: lane-level maps with submeter accuracy; real-time lane-matching based on Global Navigation Satellite Systems (GNSS) positioning with the virtual differential correction server; and experimental results demonstrating lane-matching map-matching in real-time. While this one-year effort demonstrates the feasibility of real-time lane-level map matching in one CAV application, the lane-level mapping and lane-matching technologies developed and demonstrated herein would benefit many sustainable transportation technologies. Also, the technologies involved in this project are vehicle agnostic. The project benefits will accrue to all roadway vehicles: gas or electric; commercial, public, or private; etc.