Reservation-based traffic management for autonomous intersection crossing

Choi, Mi-Young; Rubenecia, Areeya; Choi, Hyo Hyun

doi:10.1177/1550147719895956

Cited by 18 publications

(6 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tree search gives a solution to find a near-optimal schedule in feasible time, where the leaf nodes of tree enumerates all the possible schedules and the invalid nodes will be removed based on certain rules. It was first presented in [20], and subsequent studies tried more efficient tree construction and search including red-black tree [21], adaptive belief tree [22], and Monte-Carlo tree search (MCTS) [23]. The graph-based AIM models the problem of passing order as a graph, where the nodes represent vehicles or states of vehicles and the edges represent the precedence [24][25][26][27][28].…”

Section: Literature Reviewmentioning

confidence: 99%

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

Li¹,

Wu²,

He³

2023

Preprint

View full text Add to dashboard Cite

Autonomous Intersection Management (AIM) provides a signal-free intersection scheduling paradigm for Connected Autonomous Vehicles (CAVs). Distributed learning method has emerged as an attractive branch of AIM research. Compared with centralized AIM, distributed AIM can be deployed to CAVs at a lower cost, and compared with rule-based and optimization-based method, learningbased method can treat various complicated real-time intersection scenarios more flexibly. Deep reinforcement learning (DRL) is the mainstream approach in distributed learning to address AIM problems. However, the large-scale simultaneous interactive decision of multiple agents and the rapid changes of environment caused by interactions pose challenges for DRL, making its reward curve oscillating and hard to converge, and ultimately leading to a compromise in safety and computing efficiency. For this, we propose a non-RL learning framework, called Distributed Hierarchical Adversarial Learning (D-HAL). The framework includes an actor network that generates the actions of each CAV at each step. The immediate discriminator evaluates the interaction performance of the actor network at the current step, while the final discriminator makes the final evaluation of the overall trajectory from a series of interactions. In this framework, the long-term outcome of the behavior no longer motivates the actor network in terms of discounted rewards, but rather through a designed adversarial loss function with discriminative labels. The proposed model is evaluated at a four-way-six-lane intersection, and outperforms several state-of-the-art methods on ensuring safety and reducing travel time.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

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

Li¹,

Wu²,

He³

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Having received the requests, the BATCH algorithm heuristically re-ordered the vehicle's arrival at the interaction after a period of time instead of immediate assignment of the velocity to them in order to achieve safe and more efficient scheduling output. Furthermore, in [198] a heuristic approach was presented introducing a singular entrance scheduling scheme based on the reservation at intersections. To find the optimal sequence of vehicles arrival, a genetic algorithm was used and also vehicles were allowed to approach the intersection with the desired speed.…”

Section: Safety and Efficiencymentioning

confidence: 99%

A Comprehensive Survey on Cooperative Intersection Management for Heterogeneous Connected Vehicles

Gholamhosseinian

Seitz

2022

IEEE Access

View full text Add to dashboard Cite

Nowadays, with the advancement of technology, world is trending toward high mobility and dynamics. In this context, intersection management (IM) as one of the most crucial elements of the transportation sector demands high attention. Today, road entities including infrastructures, vulnerable road users (VRUs) such as motorcycles, moped, scooters, pedestrians, bicycles, and other types of vehicles such as trucks, buses, cars, emergency vehicles, and railway vehicles like trains or trams are able to communicate cooperatively using vehicle-to-everything (V2X) communications and provide traffic safety, efficiency, infotainment and ecological improvements. In this paper, we take into account different types of intersections in terms of signalized, semi-autonomous (hybrid) and autonomous intersections and conduct a comprehensive survey on various intersection management methods for heterogeneous connected vehicles (CVs). We consider heterogeneous classes of vehicles such as road and rail vehicles as well as VRUs including bicycles, scooters and motorcycles. All kinds of intersection goals, modeling, coordination architectures, scheduling policies are thoroughly discussed. Signalized and semi-autonomous intersections are assessed with respect to these parameters. We especially focus on autonomous intersection management (AIM) and categorize this section based on four major goals involving safety, efficiency, infotainment and environment. Each intersection goal provides an in-depth investigation on the corresponding literature from the aforementioned perspectives. Moreover, robustness and resiliency of IM are explored from diverse points of view encompassing sensors, information management and sharing, planning universal scheme, heterogeneous collaboration, vehicle classification, quality measurement, external factors, intersection types, localization faults, communication anomalies and channel optimization, synchronization, vehicle dynamics and model mismatch, model uncertainties, recovery, security and privacy.INDEX TERMS Vehicular ad-hoc networks (VANETs), intersection management (IM), vehicle-tovehicle (V2V), vehicle-to-infrastructure (V2I), trajectory planning (TP), spatio-temporal (ST), connected autonomous vehicles (CAVs), vulnerable road users (VRUs), connected vehicles (CVs), autonomous vehicle (AVs). II. INTERSECTION MANAGEMENT (IM) FUNDAMENTALS A. ARCHITECTURE ALTERNATIVESASHKAN GHOLAMHOSSEINIAN (Graduate Student Member, IEEE) received the bachelor's degree in computer engineering-software in Iran, the M.Sc. degree in computer network engineering from Halmstad University, Sweden, and the Post master's degree in communications for intelligent transport systems from Eurecom, France. He is currently pursuing the Ph.D. degree in electrical engineering and information technology with the Communication Networks Group, Technische Universität Ilmenau, Germany. His research interests include vehicular networks, intelligent transport systems, computer networks, and wireless and mobile networks and communications. JOCHEN...

show abstract

“…The autonomous intersection management (AIM) of the centralized system follows the server-client scheme, which can be divided into two types: request-based intersection management and release-based intersection management. In [7], [8], a request-based intersection management scheme is proposed. In this scheme, the intersection is divided into the queuing area and acceleration area.…”

Section: A Unsignalized Autonomous Intersection Managementmentioning

confidence: 99%

Unsignalized Intersection Management Strategy for Mixed Autonomy Traffic Streams

Zhou¹,

Shen²,

Wang³

et al. 2022

Preprint

View full text Add to dashboard Cite

With the rapid development of connected and automated vehicles (CAVs) and intelligent transportation infrastructure, CAVs and connected human-driven vehicles (CHVs) will coexist on the roads in the future for a long time. This paper comprehensively considers the different traffic characteristics of CHVs and CAVs, and systemically investigates the unsignalized intersection management strategy from upper decision-making level to lower execution level. Combined with the designed vehicle planning and control algorithm, the unsignalized intersection management strategy consists of two parts: the heuristic priority queues based right of way allocation (HPQ) algorithm, and the vehicle planning and control algorithm. In the HPQ algorithm, a vehicle priority management model considering the difference between CAVs and CHVs is built to design the right of way management for CAVs and CHVs, respectively. In the lower level for vehicle planning and control algorithm, different control modes of CAVs are designed according to the upper level decision made by the HPQ algorithm. Moreover, the vehicle control execution is realized by the model predictive controller combined with the geographical environment constraints and the unsignalized intersection management strategy. The proposed strategy is evaluated by simulations, which show that the proposed intersection management strategy can effectively reduce travel time and improve traffic efficiency. The intersection management strategy captures the real-world balance between efficiency and safety for potential future intelligent traffic systems.

show abstract

Reservation-based traffic management for autonomous intersection crossing

Cited by 18 publications

References 13 publications

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

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

A Comprehensive Survey on Cooperative Intersection Management for Heterogeneous Connected Vehicles

Unsignalized Intersection Management Strategy for Mixed Autonomy Traffic Streams

Contact Info

Product

Resources

About