Modeling adaptive platoon and reservation‐based intersection control for connected and autonomous vehicles employing deep reinforcement learning

Abstract

As a cutting-edge strategy to reduce travel delay and fuel consumption, platooning of connected and autonomous vehicles (CAVs) at signal-free intersections has become increasingly popular in academia. However, when determining optimal platoon size, few studies have attempted to comprehensively consider the relations between the size of a CAV platoon and traffic conditions around an intersection. To this end, this study develops an adaptive platoonbased autonomous intersection control model, named INTEL-PLT, which adopts deep reinforcement learning technique to realize the optimization of multiple dynamic objectives (e.g., efficiency, fairness, and energy saving). The framework of INTEL-PLT has a two-level structure: The first level employs a reservationbased policy integrated with a nonconflicting lane selection mechanism to determine the lanes' releasing priorities; and the second level uses a deep Qnetwork algorithm to identify the optimal platoon size based on real-time traffic conditions (e.g., traffic density, vehicle movement, etc.) of an intersection. The model is validated and examined on the simulator Simulation of Urban Mobility. It is found that the proposed model exhibits superior performances on both travel efficiency and fuel conservation as compared with state-of-the-art methods in three typical traffic conditions. Moreover, several in-depth insights learned from the simulations are provided in this paper, which could better explain the relation between platoon size and traffic condition.

D-HAL: Distributed Hierarchical Adversarial Learning for Multi-Agent Interaction in Autonomous Intersection Management

“…As a fundamental variable in the platoon-based control, the platoon size will affect the stability of platoon and the efficiency of passage in AIM, which is systematically analysed in [4]. And Li et al adopted Deep Q learning to determine the optimal platoon size in AIM [5]. Apart from the platoon formation among the adjacent vehicles in the same lanes, virtual platoon maps two-dimensional traffic flows in AIM to one-dimensional queue according to their distance to the center of the intersection, and groups non-conflicting vehicles to cross the intersection simultaneously [6,7].…”

Virtual‐real‐fusion simulation framework for evaluating and optimizing small‐spatial‐scale placement of cooperative roadside sensing units

“…Based on the high-quality and fine-grained path data provided by connected and autonomous vehicles (CAV), and Shi, Nie, et al (2022) proposed deep reinforcement learning methods to control CAV and buses, respectively, in a distributed manner. Likewise, Li et al (2023) develop a deep reinforcement learning-based model to control CAV platoon at signal-free intersections.…”

“…Likewise, Li et al. (2023) develop a deep reinforcement learning‐based model to control CAV platoon at signal‐free intersections.…”

A spatiotemporal control method at isolated intersections under mixed‐autonomy traffic conditions

“…CAVs not only provide high‐resolution, multidimensional data for traffic control (Massaro et al., 2017; Z. Zhang et al., 2022) but also render it possible to jointly optimize traffic signals and vehicle trajectories (Q. Guo et al., 2019). Numerous control methods have emerged, with a common goal of enhancing signal operations through the consideration of approaching vehicle trajectories (Feng et al., 2015; W. Li & Ban, 2019; D. Li et al., 2023; Liang et al., 2020; W. Ma et al., 2020; Wang et al., 2021) or planning vehicle trajectories based on signal status (He et al., 2015; S. E. Li et al., 2015; Stebbins et al., 2017; X. Wu et al., 2015; Zhou et al., 2017). Furthermore, the other studies focus on the joint optimization of traffic signals and vehicle trajectories, known as signal‐vehicle coupled control (SVCC; Feng et al., 2018; Y. Guo et al., 2019; Z. Li et al., 2014; Liu et al., 2022; C. Ma et al., 2023; Soleimaniamiri et al., 2020; Yu et al., 2018).…”