基于车联网的高速公路行车风险感知方法研究  

Research on highway driving risk perception based on vehicular ad hoc networks

作　　者：王靖飞 WANG Jingfei(Jiangsu Jinghu Expressway Co.,Ltd.,Nanjing 210000,China)

机构地区：[1]江苏京沪高速公路有限公司,江苏南京210000

出　　处：《中国高新科技》2024年第22期34-36,共3页

摘　　要：文章提出一种基于混合粗糙集和遗传算法优化的支持向量机(SVM)模型,用于车联网环境下的高速公路行车风险感知。通过对采集的BSM数据进行预处理和特征提取,并利用遗传算法优化SVM参数,构建了一个高效的行车状态判别模型。实车测试结果显示,该模型在检测急加速、急刹车、换道和颠簸等潜在风险行车状态时具有较高的准确性,分类正确率达94.4444%,优于传统方法,验证了其在实际应用中的有效性和鲁棒性。This paper proposes a method for highway driving risk perception based on a hybrid rough set and genetic algorithm optimized support vector machine(SVM)model in vehicular ad hoc network environments.By preprocessing and feature extraction of collected basic safety message(BSM)data,and optimizing SVM parameters using a genetic algorithm,an efficient driving state discrimination model is constructed.Real-world testing results show that the model has high accuracy in detecting potential risky driving behaviors such as abrupt acceleration,abrupt braking,lane changing,and bumpiness,with a classification accuracy of 94.4444%,surpassing traditional methods,verifying its effectiveness and robustness in practical applications.

关 键 词：车联网 高速公路 行车风险感知 

分 类 号：U495[交通运输工程—交通运输规划与管理]