Machine learning

Function (biology)

Programming language

Engineering

Artificial intelligence

Set (abstract data type)

Stability (learning theory)

Evolutionary biology

Biology

Cruise control

Road traffic control

Computer science

Control (management)

Platoon

Cooperative Adaptive Cruise Control

Computer network

Traffic flow (computer networking)

Control theory (sociology)

IEEE Transactions on Intelligent Transportation Systems

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 of noncooperative formation when AVs adopt typical Adaptive Cruise Control (ACC) strategies. Then, we focus on the case of cooperative formation where the AV controllers are cooperatively designed %redesign 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, emerge 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.

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