A Trusted Supervision Paradigm for Autonomous Driving Based on Multimodal Data Authentication
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Published:2024-09-02
Issue:9
Volume:8
Page:100
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ISSN:2504-2289
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Container-title:Big Data and Cognitive Computing
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language:en
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Short-container-title:BDCC
Author:
Shi Tianyi1, Wu Ruixiao1, Zhou Chuantian1, Zheng Siyang1, Meng Zhu1ORCID, Cui Zhe1, Huang Jin23ORCID, Ren Changrui23, Zhao Zhicheng14ORCID
Affiliation:
1. School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Haidian District, Beijing 100876, China 2. Beijing Academy of Blockchain and Edge Computing, Haidian District, Beijing 100085, China 3. Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Haidian District, Beijing 100085, China 4. Beijing Key Laboratory of Network System and Network Culture, Haidian District, Beijing 100876, China
Abstract
At the current stage of autonomous driving, monitoring the behavior of safety stewards (drivers) is crucial to establishing liability in the event of an accident. However, there is currently no method for the quantitative assessment of safety steward behavior that is trusted by multiple stakeholders. In recent years, deep-learning-based methods can automatically detect abnormal behaviors with surveillance video, and blockchain as a decentralized and tamper-resistant distributed ledger technology is very suitable as a tool for providing evidence when determining liability. In this paper, a trusted supervision paradigm for autonomous driving (TSPAD) based on multimodal data authentication is proposed. Specifically, this paradigm consists of a deep learning model for driving abnormal behavior detection based on key frames adaptive selection and a blockchain system for multimodal data on-chaining and certificate storage. First, the deep-learning-based detection model enables the quantification of abnormal driving behavior and the selection of key frames. Second, the key frame selection and image compression coding balance the trade-off between the amount of information and efficiency in multiparty data sharing. Third, the blockchain-based data encryption sharing strategy ensures supervision and mutual trust among the regulatory authority, the logistic platform, and the enterprise in the driving process.
Funder
Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing BUPT Innovation and Entrepreneurship Support Program
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