Fast and Progressive Misbehavior Detection in Internet of Vehicles Based on Broad Learning and Incremental Learning Systems

Wang, Xiao; Zhu, Yushan; Han, Sunyoung; Yang, Linyao; Gu, Haixia; Wang, Fei‐Yue

doi:10.1109/jiot.2021.3109276

Cited by 26 publications

(4 citation statements)

References 59 publications

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“…Research has indicated that collaborative systems leveraging data from interconnected vehicles and infrastructures have the potential to enhance safety and efficiency, a concept particularly relevant in the context of electric vehicles (EVs) [21]. Recent studies have explored collaborative systems for anomaly detection and misbehaviour management in autonomous vehicles, where they identify and prevent inappropriate or malicious actions within the Internet of Vehicles (IoV) system [22]. This is crucial for ensuring the system's safety and reliability, especially as EVs become more integrated into transportation networks.…”

Section: Collaborative Response Systemsmentioning

confidence: 99%

Collaborative Misbehaviour Response System for Improving Road Safety

Chikh,

Shrivastav,

Cavicchioli

2024

WEVJ

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This paper advocates for a proactive approach to traffic safety by introducing a collaborative Misbehaviour Response System (MBR) designed to preemptively address hazardous driving behaviours such as wrong-way driving and distracted driving. The system integrates with electric vehicles (EVs), leveraging advanced technologies like ADAS, edge computing, and cloud services to enhance road safety. Upon detection of misbehaviour, the MBR system utilizes data from interconnected parking facilities to identify the nearest safe location and provides navigation guidance to authorities and nearby vehicles. The paper presents a prototype of the MBR system, demonstrating its efficiency in detecting misbehaviours and coordinating swift responses. It also discusses the system’s limitations and societal implications, outlining future research directions, including integration with autonomous vehicle systems and variable speed limit technologies, to further improve road safety through proactive and context-aware response mechanisms.

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Section: Collaborative Response Systemsmentioning

confidence: 99%

Collaborative Misbehaviour Response System for Improving Road Safety

Chikh,

Shrivastav,

Cavicchioli

2024

WEVJ

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“…These studies demonstrate that the correlation is useful, but not more robust than the previously discussed features. [119] utilized the broad learning and incremental learning system (BLILS) to recognize vehicle misbehavior using the vehicle steering information, show that the proposed BLILS can recognize distractions faster and more accurately than the conventional ML and DL methods, while possessing excellent robustness and scalability for practical applications. [120] presented a driver workload detection approach based on the driver's physiological, and vehicle signals as well as traffic contexts such as congestion level and traffic events, and evaluated the proposed method on the real driving scenarios data.…”

Section: A Driver Distraction Detectionmentioning

confidence: 99%

Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles

Lou

Xing

et al. 2022

IEEE Trans. Intell. Veh.

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100

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Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the design of the automated vehicle, whereas the digitization of the human driver, who plays an important role in driving, is largely ignored. Furthermore, previous driver-related tasks are limited to specific scenarios and have limited applicability. Thus, a novel concept of a driver digital twin (DDT) is proposed in this study to bridge the gap between existing automated driving systems and fully digitized ones and aid in the development of a complete driving human cyber-physical system (H-CPS). This concept is essential for constructing a harmonious human-centric intelligent driving system that considers the proactivity and sensitivity of the human driver. The primary characteristics of the DDT include multimodal state fusion, personalized modeling, and time variance. Compared with the original DT, the proposed DDT emphasizes on internal personality and capability with respect to the external physiological-level state. This study systematically illustrates the DDT and outlines its key enabling aspects. The related technologies are comprehensively reviewed and discussed with a view to improving them by leveraging the DDT. In addition, the potential applications and unsettled challenges are considered. This study aims to provide fundamental theoretical support to researchers in determining the future scope of the DDT system.

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“…In addition, Wang et al proposed that the concept of a generalized learning system (BLS) be introduced into IoV, and the incremental learning algorithm is used to update and improve according to the newly generated data. This method has strong scalability [30]. Like Cui et al [29], they didn't consider data security.…”

Section: Incremental Data and Data Diversity In Iovmentioning

confidence: 99%

A Federated Learning Framework Based on Incremental Weighting and Diversity Selection for Internet of Vehicles

et al. 2022

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With the rapid increase of data, centralized machine learning can no longer meet the application requirements of the Internet of Vehicles (IoV). On the one hand, both car owners and regulators pay more attention to data privacy and are unwilling to share data, which forms the isolated data island challenge. On the other hand, the incremental data generated in IoV are massive and diverse. All these issues have brought challenges of data increment and data diversity. The current common federated learning or incremental learning frameworks cannot effectively integrate incremental data with existing machine learning (ML) models. Therefore, this paper proposes a Federated Learning Framework Based on Incremental Weighting and Diversity Selection for IoV (Fed-IW&DS). In Fed-IW&DS, a vehicle diversity selection algorithm was proposed, which uses a variety of performance indicators to calculate diversity scores, effectively reducing homogeneous computing. Also, it proposes a vehicle federated incremental algorithm that uses an improved arctangent curve as the decay function, to realize the rapid fusion of incremental data with existing ML models. Moreover, we have carried out several sets of experiments to test the validity of the proposed Fed-IW&DS framework’s performance. The experimental results show that, under the same global communication round and similar computing time, the Fed-IW&DS framework has significantly improved performance in all aspects compared to the frameworks FED-AVG, FED-SGD, FED-prox & the decay functions linear, square curve and arc tangent. Specifically, the Fed-IW&DS framework improves the Acc (accuracy), loss (loss), and Matthews correlation coefficient (MCC) by approximately 32%, 83%, and 66%, respectively. This result shows that Fed-IW&DS is a more reliable solution than the common frameworks of federated learning, and it can effectively deal with the dynamic incremental data in the IoV scenario. Our findings should make a significant contribution to the field of federated learning.

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Fast and Progressive Misbehavior Detection in Internet of Vehicles Based on Broad Learning and Incremental Learning Systems

Cited by 26 publications

References 59 publications

Collaborative Misbehaviour Response System for Improving Road Safety

Collaborative Misbehaviour Response System for Improving Road Safety

Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles

A Federated Learning Framework Based on Incremental Weighting and Diversity Selection for Internet of Vehicles

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