Assessing Pavement Friction Need for Safe Integration of Autonomous Vehicles into Current Road System

Abstract

Safe integration of autonomous vehicles (AVs) into the current public road system may imply potential improvements to roadway infrastructure. However, the current research effort is lacking in one of the important infrastructure to vehicle (I2V) areas, that is, roadway conditions, especially pavement friction. Motivated by the fact that pavement friction plays a critical role in safe driving for conventional vehicles and the fact that rear-end crashes are the most common type of crashes involving autonomous vehicles, this paper presents a first-of-its-kind effort to evaluate the driving safety of AVs with respect to pavement friction. An explicit relation was derived between pavement friction and traffic safety in terms of the stopping sight distance. The statistical characteristics of pavement network friction were determined from field friction measurements. A typical scenario was created to define rear-end crashes involving self-driving vehicles. The probabilities of rear-end crashes were estimated using the Monte Carlo method, and their implications were discussed. It was found that there is no urgent need to increase pavement friction requirements with respect to rear-end crashes involving self-driving vehicles. The findings may also be used to improve the extent of AV operational domains (ODs) and facilitate highway agencies to assess the readiness of road infrastructure in the form of roadway condition to support autonomous driving.