Data Validity Analysis Based on Reinforcement Learning for Mixed Types of Anomalies Coexistence in Intelligent Connected Vehicle (ICV)
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Published:2024-01-21
Issue:2
Volume:13
Page:444
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ISSN:2079-9292
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Container-title:Electronics
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language:en
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Short-container-title:Electronics
Author:
Gao Jiahao1ORCID, Hu Chuangye1, Wang Luyao1, Ding Nan12
Affiliation:
1. College of Computer Science and Technology, Xinjiang Normal University, Urumqi 830054, China 2. Key Laboratory of Intelligent Control and Optimization for Industrial Equipment, Dalian University of Technology, Dalian 116024, China
Abstract
Compared with traditional anomaly analysis, intelligent connected vehicle (ICV) data validity analysis is faced with a variety of data anomalies, including sensor anomalies, driving behavior anomalies, malicious tampering, and so on, which eventually leads to anomalies in the data. How to integrate the vehicle moving characteristics, driving style, and traffic flow conditions to provide an effective data detection method has become a new problem in the field of intelligent networked vehicles. Based on ICV data, a particle swarm optimization data validity detection algorithm (TE-PSO-SVM) was proposed by combining driving style and traffic flow theory to realize the effective detection of driving data. In addition, aiming at the problem of mixed types of anomalies in complex scenes, a model pool is constructed, and a model selection algorithm based on reinforcement learning (RLBMS) is proposed. Experiments on the real data set HighD show that RLBMS has a better detection effect in complex scenes of mixed types of anomalies.
Funder
Xinjiang Uygur Autonomous Region Natural Science Foundation Project National Natural Science Foundation of China
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
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