An Approximate Dynamic Programming Approach to Vehicle Platooning Coordination in Networks

Platooning connected and autonomous vehicles (CAVs) provide significant benefits in terms of traffic efficiency and fuel economy. However, most existing platooning systems assume the availability of pre-determined plans, which is not feasible in real-time scenarios. In this paper, we address this issue in time-dependent networks by formulating a Markov decision process at each junction, aiming to minimize travel time and fuel consumption. Initially, we analyze coordinated platooning without routing to explore the cooperation among controllers on an identical path. We propose two novel approaches based on approximate dynamic programming, offering suboptimal control in the context of a stochastic finite horizon problem. The results demonstrate the superiority of the approximation in the policy space. Furthermore, we investigate platooning in a network setting, where speed profiles and routes are determined simultaneously. To simplify the problem, we decouple the action space by prioritizing routing decisions based on travel time estimation. We subsequently employ the aforementioned policy approximation to determine speed profiles, considering essential parameters such as travel times. Our simulation results in SUMO indicate that our method yields better performance than conventional approaches, leading to potential travel cost savings of up to 38%. Additionally, we evaluate the resilience of our approach in dynamically changing networks, affirming its ability to maintain efficient platooning operations.