基于PCA-SSA-XGBoost的车辆驾驶性评估  

Drivability evaluation model based on PCA-SSA-XGBoost

作　　者：吴飞[1] 王鹏程 杨康 WU Fei;WANG Peng-cheng;YANG Kang(School of Mechanical&Electrical Engineering,Wuhan University of Technology,Wuhan 430070,China)

机构地区：[1]武汉理工大学机电工程学院,武汉430070

出　　处：《吉林大学学报(工学版)》2025年第1期105-115,共11页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金面上项目(52275505)。

摘　　要：为提高车辆驾驶性评估的效率与质量,提出了一种基于主成分分析、极限梯度提升树和麻雀优化算法的驾驶性评估模型。以双离合变速箱(Dual clutch transmission,DCT)车辆动力升挡为典型工况,研究并定义了动力升挡工况下的18项客观评价指标,利用主成分分析法对客观评价指标进行约简,降低其冗余性与耦合性,优化了模型输入样本,训练极限梯度提升树模型对驾驶性主观评分进行预测,并采用麻雀算法优化极限梯度提升树的核心超参数,提高模型精度与稳定性。道路试验表明:经主成分分析约简客观评价指标后,模型评估准确率达97%,优于BPNN(90%)、SVM(91%)、ELM(92%)与SSA-XGBoost(95%)。证明了本文PCA-SSA-XGBoost模型的准确性与稳定性优于其他模型,能更有效地完成驾驶性评估。该评估模型可迁移应用于其他驾驶工况,对于解决驾驶性评估中的主客观映射问题具有应用价值。To improve the efficiency and quality of vehicle driveability evaluation,a driving evaluation model based on principal component analysis,limit gradient lifting tree and Sparrow optimization algorithm was proposed.The dynamic upshift of Dual clutch transmission(DCT) vehicle is taken as a typical working condition,and 18 objective evaluation indexes are studied and defined.Principal component analysis was used to reduce the objective evaluation index,reduce its redundancy and coupling.The Extreme Gradient Boosting algorithm model was trained to predict the subjective driving score,and the Sparrow algorithm was used to improve the accuracy and stability of the model.The road test shows that the accuracy of the model is 97%after the objective evaluation index is reduced by principal component analysis.It is better than BPNN(90%),SVM(91%),ELM(92%) and SSA-XGBoost (95%).It is proved that the accuracy and stability of the proposed PCA-SSA-XGBoost model are better than other models,and can complete the driving evaluation more effectively.The evaluation model can be applied to other driving conditions and has application value to solve the problem of subjective and objective mapping in driving evaluation.

关 键 词：驾驶性 主成分分析 麻雀算法 极限梯度提升树 

分 类 号：U461[机械工程—车辆工程]