A Lane Merge Coordination Model for a V2X Scenario

Sequeira, Luis; Szefer, Adam; Slome, Jamie; Mahmoodi, Toktam

doi:10.1109/eucnc.2019.8801987

Cited by 18 publications

(9 citation statements)

References 9 publications

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“…Initially, the data preparation process is highlighted, then two different approaches are presented. The first one is focused on the performance of different ML classifiers including Random Forest, K-Nearest Neighbours, Decision Tree and Gradient Boosting [5]. The second approach uses Dueling DQN, and its implementation is further discussed here.…”

Section: Resultsmentioning

confidence: 99%

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The role of machine learning for trajectory prediction in cooperative driving

Sequeira

Mahmoodi

2020

Proceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Netw

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In this paper, we study the role that machine learning can play in cooperative driving. Given the increasing rate of connectivity in modern vehicles, and road infrastructure, cooperative driving is a promising first step in automated driving. The example scenario we explored in this paper, is coordinated lane merge, with data collection, test and evaluation all conducted in an automotive test track. The assumption is that vehicles are a mix of those equipped with communication units on board, i.e. connected vehicles, and those that are not connected. However, roadside cameras are connected and can capture all vehicles including those without connectivity. We develop a Traffic Orchestrator that suggests trajectories based on these two sources of information, i.e. connected vehicles, and connected roadside cameras. Recommended trajectories are built, which are then communicated back to the connected vehicles. We explore the use of different machine learning techniques in accurately and timely prediction of trajectories. CCS CONCEPTS • Computing methodologies → Reinforcement learning; Machine learning; • Networks → Cloud computing.

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Section: Resultsmentioning

confidence: 99%

“…The work from [5] was used as a starting point. In this paper, the architecture for the Traffic Orchestrator was enhanced with three main changes: 1) a new message format to communicate with the latest versions of external components, 2) a mechanism for logging and monitoring and 3) an alignment with a service-oriented architecture.…”

mentioning

confidence: 99%

The role of machine learning for trajectory prediction in cooperative driving

Sequeira

Mahmoodi

2020

Proceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Netw

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“…In [123], vehicles send their location and movement information to a server through a 5G NR Uu interface, and the server, using machine learning, recommends merging maneuvers.…”

Section: V2i Event-driven Messagesmentioning

confidence: 99%

“…Vulnerable road user collision warning [121], [127] [128] [119] Infrastructure-based collision warning [120] [124] [126] [129] Infrastructure-based see-through [120] Cooperative lane merging [123] [107] Driving through intersections support [130] Warning of rogue vehicles [122] [125] Roadworks warning [103] Road condition warning [110] Table 18 Classification of contributions of surveyed papers, RSU-relayed messages.…”

Section: Specific Applications Related Papersmentioning

confidence: 99%

A survey on road safety and traffic efficiency vehicular applications based on C-V2X technologies

Soto

Calderón

Amador

et al. 2022

Vehicular Communications

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“…The authors of Reference [ 30 ] proposed a coordinated lane merging approach for a vehicle to everything (V2X) scenarios based on a centralized system for connected cars. The application comprised of a traffic orchestrator is developed to provide the trajectory recommendation to the vehicles.…”

Section: Cooperative Driving—a Comprehensive Analysis Of the Research Literaturementioning

confidence: 99%

Collaborative Autonomous Driving—A Survey of Solution Approaches and Future Challenges

Malik

Khan

El-Sayed

2021

Sensors

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Sooner than expected, roads will be populated with a plethora of connected and autonomous vehicles serving diverse mobility needs. Rather than being stand-alone, vehicles will be required to cooperate and coordinate with each other, referred to as cooperative driving executing the mobility tasks properly. Cooperative driving leverages Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication technologies aiming to carry out cooperative functionalities: (i) cooperative sensing and (ii) cooperative maneuvering. To better equip the readers with background knowledge on the topic, we firstly provide the detailed taxonomy section describing the underlying concepts and various aspects of cooperation in cooperative driving. In this survey, we review the current solution approaches in cooperation for autonomous vehicles, based on various cooperative driving applications, i.e., smart car parking, lane change and merge, intersection management, and platooning. The role and functionality of such cooperation become more crucial in platooning use-cases, which is why we also focus on providing more details of platooning use-cases and focus on one of the challenges, electing a leader in high-level platooning. Following, we highlight a crucial range of research gaps and open challenges that need to be addressed before cooperative autonomous vehicles hit the roads. We believe that this survey will assist the researchers in better understanding vehicular cooperation, its various scenarios, solution approaches, and challenges.

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A Lane Merge Coordination Model for a V2X Scenario

Cited by 18 publications

References 9 publications

The role of machine learning for trajectory prediction in cooperative driving

The role of machine learning for trajectory prediction in cooperative driving

A survey on road safety and traffic efficiency vehicular applications based on C-V2X technologies

Collaborative Autonomous Driving—A Survey of Solution Approaches and Future Challenges

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