Cooperative Formation of Autonomous Vehicles in Mixed Traffic Flow: Beyond Platooning

Abstract

Cooperative formation and control of autonomous vehicles (AVs) promise increased efficiency and safety on public roads. In mixed traffic flow consisting of AVs and human-driven vehicles (HDVs), the prevailing platooning of multiple AVs is not the only choice for cooperative formation. In this paper, we investigate how different formations of AVs impact traffic performance from a set-function optimization perspective. We first reveal a stability invariance property and a diminishing improvement property when AVs adopt typical Adaptive Cruise Control (ACC) strategies. Then, we re-design the control strategies of AVs in different formations and investigate the optimal formation of multiple AVs using set-function optimization. Two predominant optimal formations, i.e., uniform distribution and platoon formation, emerges from extensive numerical experiments. Interestingly, platooning might have the least potential to improve traffic performance when HDVs have poor string stability behavior. These results suggest more opportunities for cooperative formation of AVs, beyond platooning, in practical mixed traffic flow.

Formation Control with Lane Preference for Connected and Automated Vehicles in Multi-lane Scenarios

“…Existing coordination methods of multiple vehicles mainly focus on single-lane scenarios, e.g. driving as a platoon (Li et al, 2017;Shladover et al, 1991;Bian et al, 2019;Li et al, 2020), and intelligent intersection management where only one lane exists in each approaching direction (Xu et al, 2018;Li & Wang, 2006;Xu et al, 2019). These single-lane coordination methods have revealed that coordination of multiple vehicles has great potential to improve driving safety and traffic efficiency.…”