An Approximate Dynamic Programming Approach to Vehicle Platooning Coordination in Networks

TL;DR

This paper introduces an approximate dynamic programming method for real-time vehicle platooning in networks, optimizing routes and speeds to improve traffic flow and fuel efficiency, with simulation showing up to 40% cost savings.

Contribution

It develops novel approximate dynamic programming approaches for coordinated platooning and routing in dynamic networks, addressing real-time decision-making challenges.

Findings

Outperforms conventional methods in simulations

Achieves up to 40% travel cost savings

Maintains efficiency in dynamic network conditions

Abstract

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 40%. Additionally, we evaluate the resilience of our approach in dynamically changing networks, affirming its ability to maintain efficient platooning operations.