Cooperative perception in Vehicular Networks using Multi-Agent Reinforcement Learning

Abdel-Aziz, Mohamed K.; Samarakoon, Sumudu; Perfecto, Cristina; Bennis, Mehdi

doi:10.1109/ieeeconf51394.2020.9443539

“…Table 1 summarizes the main advantages of using DRL in applications such as cooperative driving [53][54][55][56][57]. In the case of cooperative perception, each vehicle learns through the DRL which data should be sent to the other vehicles, taking into account their needs [58]. For instance, if vehicle 1 , with a high-resolution camera, wants to send its data to vehicle 2 , equipped with a low-resolution sensor, it will prefer to send high-resolution data as vehicle 2 needs to have a better perception of its environment.…”

Section: Deep Reinforcement Learningmentioning

confidence: 99%

Performance Evaluation of Onboard Processing Capability Reduction in Cooperative Vehicles Using 5G and Artificial Intelligence

Palacios-Morocho,

Inca,

Monserrat

2024

Mobile Information Systems

0

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Fifth-generation (5G) technology is one of the keys to the Industrial Revolution known as Industry 4.0 as it provides faster connectivity and allows a greater number of devices to be connected simultaneously. In the transport sector, newly produced vehicles are equipped with various sensors and applications to help drivers perform safe maneuvers. However, moving from semiautonomous to fully autonomous vehicles to cooperating systems remains a major challenge. Many researchers have focused on artificial intelligence (AI) techniques and the ability to share information to achieve this cooperative behavior. This information can be made up of different data, which can be obtained from different sensors such as laser imaging detection and ranging (LiDAR), radar, camera, global positioning system (GPS), or data related to the current speed, acceleration, or position. The combination of the different shared data is performed depending on the approach of each navigation algorithm. This data fusion will allow a better understanding of the environment but will overload the network, as the traffic generated will be massive. Therefore, this paper addresses the challenge of achieving this cooperation between vehicles from the point of view of network requirements and computational capacity. In addition, this study contributes to advancing theory into real-world practice by examining the performance of cooperative navigation algorithms in the midst of the migration of computational resources from onboard vehicle equipment to the cloud. In particular, it investigates the transition from a cooperative navigation algorithm based on a decentralized architecture to a semidecentralized one as computationally demanding processes previously performed onboard are performed in the cloud. Additionally, the paper discusses the indispensable role of 5G in fulfilling the escalating demands for high throughput and low latency in these services, particularly as the number of vehicles increases. The results of the tests show that the AI acting alone cannot achieve optimal performance, even using 100% of the computational capacity of the onboard equipment in the vehicle. However, a system that integrates 5G and AI-based joint decisions can achieve better performance, reduce the computational resources consumed in the vehicle, and increase the efficiency of collaborative choices by up to 83.3%.

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“…It is challenging to tailor the number and resolution of transmitted quadtree blocks to bandwidth availability. In the framework of FRL, Mohamed et al present a quadtree-based point cloud compression mechanism to select cooperative perception messages [113]. Specifically, over a period of time, each vehicle covered by an RSU transfers its latest network weights with the RSU, which then averages all of the received model parameters and broadcasts the result back to the vehicles.…”

Section: E Frl For Other Applicationsmentioning

confidence: 99%

Federated Reinforcement Learning: Techniques, Applications, and Open Challenges

Qi¹,

Zhou²,

Lei³

et al. 2021

Preprint

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This paper presents a comprehensive survey of Federated Reinforcement Learning (FRL), an emerging and promising field in Reinforcement Learning (RL). Starting with a tutorial of Federated Learning (FL) and RL, we then focus on the introduction of FRL as a new method with great potential by leveraging the basic idea of FL to improve the performance of RL while preserving data-privacy. According to the distribution characteristics of the agents in the framework, FRL algorithms can be divided into two categories, i.e., Horizontal Federated Reinforcement Learning (HFRL) and Vertical Federated Reinforcement Learning (VFRL). We provide the detailed definitions of each category by formulas, investigate the evolution of FRL from a technical perspective, and highlight its advantages over previous RL algorithms. In addition, the existing works on FRL are summarized by application fields, including edge computing, communication, control optimization, and attack detection. Finally, we describe and discuss several key research directions that are crucial to solving the open problems within FRL.

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“…It is challenging to tailor the number and resolution of transmitted quadtree blocks to bandwidth availability. In the framework of FRL, Mohamed et al present a quadtree-based point cloud compression mechanism to select cooperative perception messages [112] . Specifically, over a period of time, each vehicle covered by an RSU transfers its latest network weights with the RSU, which then averages all of the received model parameters and broadcasts the result back to the vehicles.…”

Section: Frl For Other Applicationsmentioning

confidence: 99%

Federated reinforcement learning: techniques, applications, and open challenges

Qi¹,

Zhou²,

Lei³

et al. 2021

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This paper presents a comprehensive survey of Federated Reinforcement Learning (FRL), an emerging and promising field in Reinforcement Learning (RL). Starting with a tutorial of Federated Learning (FL) and RL, we then focus on the introduction of FRL as a new method with great potential by leveraging the basic idea of FL to improve the performance of RL while preserving data-privacy. According to the distribution characteristics of the agents in the framework, FRL algorithms can be divided into two categories, i.e., Horizontal Federated Reinforcement Learning (HFRL) and Vertical Federated Reinforcement Learning (VFRL). We provide the detailed definitions of each category by formulas, investigate the evolution of FRL from a technical perspective, and highlight its advantages over previous RL algorithms. In addition, the existing works on FRL are summarized by application fields, including edge computing, communication, control optimization, and attack detection. Finally, we describe and discuss several key research directions that are crucial to solving the open problems within FRL.

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Cooperative perception in Vehicular Networks using Multi-Agent Reinforcement Learning

Cited by 5 publications

References 10 publications

Performance Evaluation of Onboard Processing Capability Reduction in Cooperative Vehicles Using 5G and Artificial Intelligence

Performance Evaluation of Onboard Processing Capability Reduction in Cooperative Vehicles Using 5G and Artificial Intelligence

Federated Reinforcement Learning: Techniques, Applications, and Open Challenges

Federated reinforcement learning: techniques, applications, and open challenges

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