Strategic and tactical decision-making for cooperative vehicle platooning with organized behavior on multi-lane highways

Abstract

Driving automation and vehicle-to-vehicle (V2V) communication provide opportunities to deploy cooperative automated driving systems (C-ADS) for transportation system goals such as sustainability, safety, and efficiency. Among various C-ADS applications, vehicle platooning has great potential to achieve the above system management goals by establishing trajectory-aware V2V cooperative strategies among C-ADS vehicles. Previously, the concept of cooperative adaptive cruise control (CACC)—that is, single-lane decentralized ad-hoc operations of multiple vehicles that closely follow each other—has been studied by researchers extensively. This study builds upon the existing research and proposes a comprehensive multi-lane platooning algorithm with organized behavior via a hierarchical framework. The proposed algorithm adopts the modern state of the art (SOTA) C-ADS software platform framework, which consist of perception, plan and control levels. The multi-lane platooning algorithm incorporates both the strategic level (i.e., mission level) and the tactical level (i.e., motion level) decision-making to cope with complex multi-lane highway challenges, including same-lane platooning, multi-lane joining, and on-ramp merging. Based on the algorithm’s strategies, the platoon leaders coordinate between platoon members and external vehicles to guide the platoon through complicated and realistic driving scenarios. On the strategic mission level, a platooning behavior protocol based on a deterministic finite state machine (FSM) is developed to guide the member operations. Additionally, as heuristic protocols fall short in explicitly expressing complex cooperative scenarios, a genetic fuzzy system was trained with FSM as a baseline to extend the algorithm’s capability under the cooperative on-ramp merge scenarios. On the tactical motion level, trajectory generation for general ADS maneuvers (i.e., lane following and lane changing) and platooning behavior regulation is proposed such that planned trajectories of other relevant vehicles can be fully considered (i.e., intent sharing of predictive nature). The performance is evaluated in both traffic and automated driving simulators, and the results indicate that the proposed comprehensive multi-lane platooning algorithm can efficiently and safely regulate C-ADS-equipped vehicle behavior and meet system goals.