Does recognizability of connected and automated vehicles (CAVs) platoons affect drivers’ behavior and safety?

Abstract

Platoons of connected and automated Vehicles (CAVs) comprising both passenger cars and trucks are expected to appear on highways in the coming years. Though prior studies explored driver behaviors in the presence of CAV platoons, it remains unknown whether it is necessary for a traditional vehicle’s driver to recognize CAV platoons in a mixed traffic environment for safe maneuvering. This study aims to test the hypothesis that drivers behave differently when interacting with recognizable CAV platoons compared to interacting with non-recognizable CAV platoons. A driving simulator experiment was conducted featuring four merging and diverging scenarios. Participants were informed beforehand that blue-colored vehicles driving with short gaps would be CAV platoons. However, they also encountered CAV platoons consisting of several vehicles with different colors (not blue). The blue-colored platoon was considered as recognizable CAV platoons and the latter as non-recognizable CAV platoons. Performance indicators including speed, maximum acceleration and deceleration, standard deviation (SD) of speed and acceleration, and time-to-collision (TTC) were considered to examine drivers’ behavior. The generalized estimating equation (GEE) approach was applied to evaluate the effect of recognizability and demographic characteristics on driver performance. The GEE results revealed significant improvement in drivers’ behavior in the presence of recognizable CAV platoons. The merging SD of speed and acceleration were around 32% and 29% lower, respectively in the presence of recognizable CAV platoons. The merging TTC was around 20% higher in the presence of recognizable CAV platoons. Although no significant main effect of recognizability was found for diverging maneuvers, a noteworthy result was that male drivers had around 37% higher diverging TTC than female drivers in the presence of recognizable CAV platoons. The findings also suggested significant effects of gender, age, driving experience, and education on driver performance while merging, as well as effect of gender and prior crash involvement on driver performance while diverging. These findings provide valuable insights to transportation planning authorities in determining the potential need for making CAV platoons recognizable and developing associated guidelines for safer traffic movements in mixed traffic environments.