Informed decision-making by integrating historical on-road driving performance data in high-resolution maps for connected and automated vehicles

Abstract

Connected and automated vehicles (CAVs) are already part of the surface transportation system. In order for a CAV to operate safely, it needs information such as static data (high-resolution navigation maps) and real-time dynamics from various sensors, some of which exchange information with other vehicles or roadside units. High resolution navigation maps can integrate historical on-road driving performance data to help CAVs and drivers operating vehicles with low level automation make informed proactive decisions. This study proposes that navigation maps on CAVs come pre-installed with historical driving data and that they work together with real-time sensors to help CAVs plan maneuvers. Historical driving data offers insights about decisions made by drivers at locations along a route, e.g., where drivers often make sharp turns or where they accelerate and decelerate hard. A pre-installed record of historical driving decisions will support informed decision-making and proactively “warn” CAVs and drivers about potential hazards. This study explores location-based driving volatility as a key to improving safety through CAVs. Location-based volatility is a measure of historical driving performance, defined as the percentage of extreme maneuvers performed on a location in road network. For demonstration, we modeled and visualized real-world high-resolution geo-referenced data. The data comes from a connected vehicle safety pilot program in Ann Arbor, Michigan. We found measured location-based volatility is significantly related to safety outcomes. Therefore, location-based driving volatility can serve as a valuable piece of information to be added to navigation maps in CAVs in order to help them navigate volatile hot-spots.

Keywords:

• Connected and automated vehicles
• location-based driving volatility
• pre-installed data
• informed decision-making

Acknowledgements

This study first pulls driving data from Research Data Exchange, maintained by the Federal Highway Administration under US DOT. The data was originally collected in a connected vehicle safety pilot program, Safety Pilot Model Deployment (SPMD), in Ann Arbor, Michigan. Software packages R and Google Earth were used for the data processing, visualization, and modeling. The views expressed in this paper are those of the authors, who are responsible for the facts and accuracy of information presented herein.

Additional information

Funding

The study is supported by National Science Foundation (Award number: 1538139).