BRAEVE: Stable and adaptive BSM rate control over IEEE802.11p vehicular networks

Ogura, Kazumine; Katto, Jiro; Takai, Mineo

doi:10.1109/ccnc.2013.6488540

Cited by 9 publications

(5 citation statements)

References 4 publications

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“…The set of states for a total of n states and m actions is represented by equation (5), and the set of actions is represented by equation (6). Each state, action, and reward has a Q-function, denoted by equation (7).…”

Section: Drl (Deep Reinforcement Learning) Approachesmentioning

confidence: 99%

“…Thirdly, in the field of public transportation, overseas cases of Mobility as a Service (MaaS) are emerging, especially in the Nordic 2 region of Western Europe. MaaS provides demand-based subscriber transportation packages, offering integrated transportation information including various modes of transportation on a single platform, as well as integrated payment services [5]. It represents a departure from the existing transportation systems provided by supply-oriented providers and aims to provide personalized optimal transportation information and route systems, reservation and payment systems, and other integrated operational services from the user's perspective.…”

Section: Introductionmentioning

confidence: 99%

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SMITS: Research on Smart Mobility Intelligent Traffic Signal System based on Distributed Deep Reinforcement Learning

2023

Preprint

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Recently, smart mobility intelligent traffic services have become a critical task in Intelligent Transportation Systems (ITS). This involves not only the use of advanced sensors and controllers but also the ability to respond to real-time traffic situations at intersections, alleviate congestion, and generate policies to prevent traffic jams. DRL (Deep Reinforcement Learning) provides a natural framework for processing tasks. In DRL, each intersection can control itself and coordinate with neighbors to achieve optimal network-wide policies. However, comparing approaches remains a challenging task due to the existence of numerous possible configurations. This research performs a critical comparison of various traffic controllers found in the literature. It demonstrates that using a nonlinear approximator for coordination mechanisms and enhancing observability at each intersection are key performance drivers.

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Section: Drl (Deep Reinforcement Learning) Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SMITS: Research on Smart Mobility Intelligent Traffic Signal System based on Distributed Deep Reinforcement Learning

2023

Preprint

View full text Add to dashboard Cite

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“…In [13], the authors present a new algorithm called Error Model Based Adaptive Rate Control (EMBARC), which updates LIMERIC by also considering vehicle position error estimation. Another rate-based approach, BSM RAte control over IEEE802.11p VEhicular networks (BRAEVE) [14], uses the estimated number of vehicles rather than channel utilization as the input parameter. BRAEVE is shown to provide smoother convergence and result in lower packet error ratio, IPD and tracking error compared to the other algorithms.…”

Section: Related Work Existing Decentralized Congestion Control Approachesmentioning

confidence: 99%

“…The limited channel capacity and high message rates (to ensure an adequate level of awareness) make the reliable delivery of BSMs a challenging problem for VANET, and a number of different congestion control approaches have been proposed for vehicular communication [12]. Message transmission rate used in [10,13,14] and power control used in [15][16][17][18][19] (or a combination of both used in [20][21][22][23]) are the common parameters that are controlled to mitigate channel congestion, although other parameters, e.g., data rate, carrier sense threshold, have also been suggested [24,25]. Both message rate and power control reduce awareness of the neighboring vehicles either by increasing Inter-Packet Delay (IPD) for BSMs received from a vehicle or by limiting the distance at which such packets can be received correctly.…”

Section: Introductionmentioning

confidence: 99%

Balancing Awareness and Congestion in Vehicular Networks Using Variable Transmission Power

Liu¹,

Amour²,

Jaekel³

2021

Electronics

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Vehicular ad Hoc networks (VANETs) support a variety of applications ranging from critical safety applications to “infotainment” or “comfort” applications. In North America, 75 MHz of the spectrum in the 5.9 GHz band has been allocated for vehicular communication. Safety applications rely on event-driven “alert” messages as well as the periodic broadcast of Basic Safety Messages (BSMs) containing critical information, e.g., position, speed, and heading from participating vehicles. The limited channel capacity and high message rates needed to ensure an adequate level of awareness make the reliable delivery of BSMs a challenging problem for VANETs. In this paper, we propose a decentralized congestion control algorithm that uses variable transmission power levels to reduce the channel busy ratio while maintaining a high level of awareness for nearby vehicles. The simulation results indicate that the proposed approach is able to achieve a suitable balance between awareness and bandwidth usage.

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“…Vehicles periodically transmit BSM messages that encompass fundamental vehicle information, including position, speed, and acceleration. Given the time-critical nature and substantial volume of BSM messages, effectively managing network traffic load and mitigating congestion have emerged as pivotal research areas [10]. Furthermore, in situations where UAV resources are constrained or cost considerations come into play, multiple vehicles may require shared access to these resources.…”

Section: Introduction 1preliminariesmentioning

confidence: 99%

A Preemptive-Resume Priority MAC Protocol for Efficient BSM Transmission in UAV-Assisted VANETs

Li,

Han,

Guan

et al. 2024

Applied Sciences

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With the development and popularization of Intelligent Transportation Systems (ITS), Vehicle Ad-Hoc Networks (VANETs) have attracted extensive attention as a key technology. In order to achieve real-time monitoring, VANET technology enables vehicles to collect real-time traffic updates through information collection devices and transmit this information to Roadside Units (RSUs), which are processed and integrated by an information processing center. However, high vehicle density leads to a conflict between minimizing the interval for vehicles to send Basic Safety Messages (BSMs) to RSUs and the limited communication resources of VANETs. To address this issue, we propose a MAC protocol based on the 802.11 CSMA/CA mechanism with the Preemptive-Resume Priority scheme. The arbitration device provides preemptive service to data packets with higher priority levels, thereby reducing data transmission delay. Moreover, queuing theory is employed to calculate the total delay for vehicles to send BSMs to a drone receiver, minimizing the BSM transmission interval and achieving minimal delay to meet safety driving requirements. The effectiveness and superiority of this mechanism and algorithm are demonstrated through simulation experiments.

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BRAEVE: Stable and adaptive BSM rate control over IEEE802.11p vehicular networks

Cited by 9 publications

References 4 publications

SMITS: Research on Smart Mobility Intelligent Traffic Signal System based on Distributed Deep Reinforcement Learning

SMITS: Research on Smart Mobility Intelligent Traffic Signal System based on Distributed Deep Reinforcement Learning

Balancing Awareness and Congestion in Vehicular Networks Using Variable Transmission Power

A Preemptive-Resume Priority MAC Protocol for Efficient BSM Transmission in UAV-Assisted VANETs

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