Optimizing Coordinated Vehicle Platooning: An Analytical Approach Based on Stochastic Dynamic Programming

by   Xi Xiong, et al.

Platooning connected and autonomous vehicles (CAVs) can improve traffic and fuel efficiency. However, scalable platooning operations requires junction-level coordination, which has not been well studied. In this paper, we study the coordination of vehicle platooning at highway junctions. We consider a setting where CAVs randomly arrive at a highway junction according to a general renewal process. When a CAV approaches the junction, a system operator determines whether the CAV will merge into the platoon ahead according to the positions and speeds of the CAV and the platoon. We formulate a Markov decision process to minimize the discounted cumulative travel cost, i.e. fuel consumption plus travel delay, over an infinite time horizon. We show that the optimal policy is threshold-based: the CAV will merge with the platoon if and only if the difference between the CAV's and the platoon's predicted times of arrival at the junction is less than a constant threshold. We also propose two ready-to-implement algorithms to derive the optimal policy. Comparison with the classical value iteration algorithm implies that our approach explicitly incorporating the characteristics of the optimal policy is significantly more efficient in terms of computation. Importantly, we show that the optimal policy under Poisson arrivals can be obtained by solving a system of integral equations. We also validate our results in simulation with Real-time Strategy (RTS) using real traffic data. The simulation results indicate that the proposed method yields better performance compared with the conventional method.


page 1

page 2

page 3

page 4


A Structure-aware Online Learning Algorithm for Markov Decision Processes

To overcome the curse of dimensionality and curse of modeling in Dynamic...

Minimizing Age of Incorrect Information over a Channel with Random Delay

We investigate a transmitter-receiver pair in a slotted-time system. The...

V2I Connectivity-Based Dynamic Queue-Jumper Lane for Emergency Vehicles: An Approximate Dynamic Programming Approach

Emergency vehicle (EV) service is a key function of cities and is exceed...

Dynamic gNodeB Sleep Control for Energy-Conserving 5G Radio Access Network

5G radio access network (RAN) is consuming much more energy than legacy ...

Controlling Federated Learning for Covertness

A learner aims to minimize a function f by repeatedly querying a distrib...

MOVI: A Model-Free Approach to Dynamic Fleet Management

Modern vehicle fleets, e.g., for ridesharing platforms and taxi companie...

R-Learning Based Admission Control for Service Federation in Multi-domain 5G Networks

Service federation in 5G/B5G networks enables service providers to orche...

Please sign up or login with your details

Forgot password? Click here to reset