A Survey on Intersection Management of Connected Autonomous Vehicles

Khayatian, Mohammad; Mehrabian, Mohammadreza; Andert, Edward; Dedinsky, Rachel; Choudhary, Sarthake; Lou, Yingyan; Shirvastava, Aviral

doi:10.1145/3407903

Cited by 93 publications

(54 citation statements)

References 111 publications

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“…Empowered by IoV, the issues of safety [1] and efficiency [2] can be addressed in a cooperative manner. This study focuses on the optimization of unsignalized intersection management [3], where motions of each vehicle are precisely controlled to pass through an intersection without traffic lights.…”

Section: Introductionmentioning

confidence: 99%

A Traffic-Aware Federated Imitation Learning Framework for Motion Control at Unsignalized Intersections with Internet of Vehicles

Jiang

Han

et al. 2021

Electronics

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The wealth of data and the enhanced computation capabilities of Internet of Vehicles (IoV) enable the optimized motion control of vehicles passing through an intersection without traffic lights. However, more intersections and demands for privacy protection pose new challenges to motion control optimization. Federated Learning (FL) can protect privacy via model interaction in IoV, but traditional FL methods hardly deal with the transportation issue. To address the aforementioned issue, this study proposes a Traffic-Aware Federated Imitation learning framework for Motion Control (TAFI-MC), consisting of Vehicle Interactors (VIs), Edge Trainers (ETs), and a Cloud Aggregator (CA). An Imitation Learning (IL) algorithm is integrated into TAFI-MC to improve motion control. Furthermore, a loss-aware experience selection strategy is explored to reduce communication overhead between ETs and VIs. The experimental results show that the proposed TAFI-MC outperforms imitated rules in the respect of collision avoidance and driving comfort, and the experience selection strategy can reduce communication overheads while ensuring convergence.

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Section: Introductionmentioning

confidence: 99%

A Traffic-Aware Federated Imitation Learning Framework for Motion Control at Unsignalized Intersections with Internet of Vehicles

Jiang

Han

et al. 2021

Electronics

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“…In the case of automated driving vehicles, intersection managers are planned to prevent traffic accidents. The intersection manager can be separated into several factors such as V2X interfaces, conflict detection, and vehicle dynamics [ 27 ]. There two architectures for V2X interfaces, i.e., centralized and decentralized approaches.…”

Section: Required Data Rate On V2i For Safe Crossingmentioning

confidence: 99%

Automated Driving with Cooperative Perception Based on CVFH and Millimeter-Wave V2I Communications for Safe and Efficient Passing through Intersections

Fukatsu

Sakaguchi

2021

Sensors

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The development of automated driving is actively progressing, and connected cars are also under development. Connected cars are the technology of connecting vehicles to networks so that connected vehicles can enhance their services. Safety services are among the main services expected in connected car society. Cooperative perception belongs to safety services and improves safety by visualizing blind spots. This visualization is achieved by sharing sensor data via wireless communications. Therefore, the number of visualized blind spots highly depends upon the performance of wireless communications. In this paper, we analyzed the required sensor data rate to be shared for the cooperative perception in order to realize safe and reliable automated driving in an intersection scenario. The required sensor data rate was calculated by the combination of recognition and crossing decisions of an automated driving vehicle to adopt realistic assumptions. In this calculation, CVFH was used to derive tight requirements, and the minimum required braking aims to alleviate the traffic congestion around the intersection. At the end of the paper, we compare the required sensor data rate with the outage data rate realized by conventional and millimeter-wave communications, and show that millimeter-wave communications can support safe crossing at a realistic velocity.

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“…Namazi et al [20] also conducted a literature review on the management of signalized and non-signalized intersections under vehicular environment in a systematic way. Moreover, Khayatian et al [21] presented a survey on IM of CAVs from different aspects consisting of architecture, vehicle dynamics, wireless technologies, scheduling mechanisms, collision detection, human-operated vehicles, recovery, security, safety, robustness issues and simulation tools. Krishnan et al [22] performed a partial non technical study on IM while Guo et al [23] addressed solutions for traffic flow estimation and optimizing traffic signal timings based on CAVs at the urban signalized intersections.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey on Cooperative Intersection Management for Heterogeneous Connected Vehicles

Gholamhosseinian

Seitz

2022

IEEE Access

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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...

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A Survey on Intersection Management of Connected Autonomous Vehicles

Cited by 93 publications

References 111 publications

A Traffic-Aware Federated Imitation Learning Framework for Motion Control at Unsignalized Intersections with Internet of Vehicles

A Traffic-Aware Federated Imitation Learning Framework for Motion Control at Unsignalized Intersections with Internet of Vehicles

Automated Driving with Cooperative Perception Based on CVFH and Millimeter-Wave V2I Communications for Safe and Efficient Passing through Intersections

A Comprehensive Survey on Cooperative Intersection Management for Heterogeneous Connected Vehicles

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