Interacting with Autonomous Platoons: Human Driver’s Adaptive Behaviors in Planned Lane Changes

With an increasing reliance on autonomous driving technologies, mixed traffic is expected to emerge as a dominant mode in the traffic ecosystem of the coming future, which will drastically change how the human drivers of manual vehicles perceive and interact with autonomous vehicles. From the standpoint of human-automation interaction, a question arises whether it is humans or vehicles algorithms that should adapt themselves to the myriads of traffic situations. To address this question, the current paper recruited eleven participants to a medium-fidelity driving simulation study, and collected driving performance and gaze behaviors during the planned lane changes when they were expected to cut their ways through vehicle platoons of different time headways. The results showed a varying degree of adaptive behaviors and risk attitudes from participants in response to the different headways during the lane change. Due to this large individual variation in mixed traffic interaction, highly adaptive algorithm for autonomous platoon is much desired. In this regard, some behavioral markers for planned lane change was recommended in this paper.