重庆大学学报2025，Vol.48Issue(8)：99-110,12.DOI:10.11835/j.issn.1000-582X.2025.08.009
基于机器学习的汽车智能座舱告警筛选系统
Machine learning-based intelligent cabin alert filtering system for vehicles
摘要
Abstract
This study presents a machine learning-based intelligent cabin alert filtering system for vehicles aiming to address safety risks caused by excessive and redundant alarm sources.To overcome limitations in current systems,such as alarm redundancy and inaccurate classifications,a hybrid selection strategy is proposed that combines manual expert filtering with a convolutional neural network(CNN)model.The system integrates operational data from various devices,applying manual heuristics to eliminate likely false signals and employing the CNN model for robust feature extraction and precise classification.Experimental results show that the CNN model achieves a classification accuracy of 89.07%on the test dataset.When combined with manual filtering,the overall selection accuracy of alarm signals reaches 99.998%,significantly surpassing the conventional VAS system(90%).These results validate the proposed method's effectiveness in filtering alarm information.Future research will focus on expanding training datasets,optimizing model parameters,and improving text pre-processing techniques to further enhance the overall system performance.关键词
机器学习技术/智能座舱告警/告警源/CNNKey words
machine learning/intelligent cabin alarms/alarm filtering/CNN分类
交通工程引用本文复制引用
张莹,袁海兵,何祺,姜立标,陈毅锋,陈桥芳..基于机器学习的汽车智能座舱告警筛选系统[J].重庆大学学报,2025,48(8):99-110,12.基金项目
国家自然科学基金(61602345).Supported by National Natural Science Foundation of China(61602345). （61602345）
