A Multi-Intersection Vehicular Cooperative Control Based on End-Edge-Cloud Computing

Jiang, Mingzhi; Wu, Tianhao; Wang, Zhe; Gong, Yi; Zhang, Lin; Liu, Ren Ping

doi:10.1109/tvt.2022.3143828

Cited by 12 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, UBI data processing architecture is essential in the implementation of vehicle UBI products. Basically, the architecture should include an on-board data transceiver module for user-driving data acquisition, a data transmission channel, and a remote data center for data analysis and application, during recent years, in combination with the latest edge computing technology, some scholars have also transformed this architecture as "endedge-cloud" based on the concept of IoV [128], its system structure for vehicle UBI products can be shown in Figure 3. In terms of the implementation of vehicle UBI products, it should also be pointed out that with the gradual popularization of semi-autonomous and autonomous vehicles, they are gradually bringing changes to the commercial vehicle insurance industry.…”

Section: Research On the Architecture Of Data Processing System For V...mentioning

confidence: 99%

A Comprehensive Review of Driving Style Evaluation Approaches and Product Designs Applied to Vehicle Usage-Based Insurance

Nai¹,

Yang²,

Wei³

et al. 2022

Sustainability

View full text Add to dashboard Cite

Vehicle insurance is a very important source of income for insurance companies, and it is closely related to the driving style performed by driving behavior. Different driving styles can better reflect the driving risk than the number of violations, claims, and other static statistic data. Subdivide the vehicle insurance market according to the personal characteristics and driving habits of the insured vehicles, and studying the personalized vehicle insurance products, will help the insurance companies to improve their income, help the drivers to change their bad driving habits, and thus help to realize the healthy development of the vehicle insurance industry. In the past 20 to 30 years, more and more insurance companies around the world have launched vehicle usage-based insurance (UBI) products based on driving style analysis. However, up to now, there are few comprehensive reports on commercial vehicle UBI products and their core driving risk assessment methods. On the basis of literature indexing on the Web of Science and other academic platforms by using the keywords involved in vehicle UBI, over 100 relevant works of literature were screened in this paper, and a detailed and comprehensive discussion on the driving style evaluation methods and the design of commercial vehicle UBI products during the past 20 to 30 years has been made, hoping to get a full understanding of the possible factors affecting driving style and the collectible data that can reflect these factors, and to get a full grasp of the developing status, challenges and future trends in vehicle insurance branch of the Internet of Vehicles (IoV) industry.

show abstract

Section: Research On the Architecture Of Data Processing System For V...mentioning

confidence: 99%

A Comprehensive Review of Driving Style Evaluation Approaches and Product Designs Applied to Vehicle Usage-Based Insurance

Nai¹,

Yang²,

Wei³

et al. 2022

Sustainability

View full text Add to dashboard Cite

show abstract

“…[14] and [15] studied distributed cooperation methods to realize the conflictfree control of CAVs. [16] and [17] implemented a multiagent systems-based hierarchical controller to improve vertical and horizontal cooperation among the automated vehicles. It is noted that all the above MARL algorithms for CAVs were designed to improve driving safety.…”

Section: Introductionmentioning

confidence: 99%

Progression Cognition Reinforcement Learning With Prioritized Experience for Multi-Vehicle Pursuit

Li,

Yang,

Yuan

et al. 2024

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

Multi-vehicle pursuit (MVP) such as autonomous police vehicles pursuing suspects is important but very challenging due to its mission and safety-critical nature. While multiagent reinforcement learning (MARL) algorithms have been proposed for MVP in structured grid-pattern roads, the existing algorithms use random training samples in centralized learning, which leads to homogeneous agents showing low collaboration performance. For the more challenging problem of pursuing multiple evaders, these algorithms typically select a fixed target evader for pursuers without considering dynamic traffic situation, which significantly reduces pursuing success rate. To address the above problems, this paper proposes a Progression Cognition Reinforcement Learning with Prioritized Experience for MVP (PEPCRL-MVP) in urban multi-intersection dynamic traffic scenes. PEPCRL-MVP uses a prioritization network to assess the transitions in the global experience replay buffer according to each MARL agent's parameters. With the personalized and prioritized experience set selected via the prioritization network, diversity is introduced to the MARL learning process, which can improve collaboration and task-related performance. Furthermore, PEPCRL-MVP employs an attention module to extract critical features from dynamic urban traffic environments. These features are used to develop a progression cognition method to adaptively group pursuing vehicles. Each group efficiently targets one evading vehicle. Extensive experiments conducted with a simulator over unstructured roads of an urban area show that PEPCRL-MVP is superior to other state-of-the-art methods. Specifically, PEPCRL-MVP improves pursuing efficiency by 3.95% over Twin Delayed Deep Deterministic policy gradient-Decentralized Multi-Agent Pursuit and its success rate is 34.78% higher than that of Multi-Agent Deep Deterministic Policy Gradient. Codes are open-sourced.

show abstract

Delay Optimization for Consensus Communication in Blockchain-Based End-Edge-Cloud Network

Ma,

Wang,

Tsai

et al. 2023

Lecture Notes in Computer Science

View full text Add to dashboard Cite

A Multi-Intersection Vehicular Cooperative Control Based on End-Edge-Cloud Computing

Cited by 12 publications

References 25 publications

A Comprehensive Review of Driving Style Evaluation Approaches and Product Designs Applied to Vehicle Usage-Based Insurance

A Comprehensive Review of Driving Style Evaluation Approaches and Product Designs Applied to Vehicle Usage-Based Insurance

Progression Cognition Reinforcement Learning With Prioritized Experience for Multi-Vehicle Pursuit

Delay Optimization for Consensus Communication in Blockchain-Based End-Edge-Cloud Network

Contact Info

Product

Resources

About