Interaction-aware model predictive control for automated vehicles in mixed-autonomy traffic

Automated vehicles (AVs) hold the potential to significantly improve traffic flow, reducing travel time, energy consumption, and emissions. However, until AVs achieve high market penetration rates, navigating the transition to mixed-autonomy traffic — comprising both AVs and human-driven vehicles (HVs) — presents substantial challenges. While numerous studies have concentrated on AV control within mixed-autonomy environments, human-AV interactions have been largely neglected. To understand the benefits of considering the impact of AVs on their followers in mixed traffic control, we introduce a general framework focused on social interaction-aware benefits. Through this framework, we develop an interaction-aware control approach aimed at optimizing socially compatible traffic flow. The results demonstrate that as social interactions between the AV and its following HVs are considered, the benefits (i.e., vehicle speed mean squared error) for the AV may decrease. In contrast, HVs can gain more benefits when the interaction-aware control strategy is not solely focused on the AV.