Modeling Adaptive Platoon and Reservation Based Autonomous Intersection Control: A Deep Reinforcement Learning Approach

TL;DR

This paper introduces a deep reinforcement learning-based model for adaptive autonomous intersection control that optimizes platoon size and vehicle sequencing to improve traffic flow and energy efficiency.

Contribution

It presents a novel two-level framework combining reservation-based lane selection with DRL to adaptively determine platoon sizes based on real-time traffic conditions.

Findings

Outperforms existing methods in travel efficiency

Reduces fuel consumption in simulated tests

Adapts platoon size dynamically to traffic conditions

Abstract

As a strategy to reduce travel delay and enhance energy efficiency, platooning of connected and autonomous vehicles (CAVs) at non-signalized intersections has become increasingly popular in academia. However, few studies have attempted to model the relation between the optimal platoon size and the traffic conditions around the intersection. To this end, this study proposes an adaptive platoon based autonomous intersection control model powered by deep reinforcement learning (DRL) technique. The model framework has following two levels: the first level adopts a First Come First Serve (FCFS) reservation based policy integrated with a nonconflicting lane selection mechanism to determine vehicles' passing priority; and the second level applies a deep Q-network algorithm to identify the optimal platoon size based on the real-time traffic condition of an intersection. When being tested on a traffic micro-simulator, our proposed model exhibits superior performances on travel efficiency and fuel conservation as compared to the state-of-the-art methods.