CAJ | 학술논문

为了在车辆测试中融入不同的驾驶风格而使测试结果更接近于真实，提出一种基于汽车测试数据和神经网络的驾驶员建模方法。考虑到实际数据的高度离散性和局部突变性，以局部性神经网络的典型代表小脑模型关节控制器(Cerebellar model articulation controller，CMAC)网络为基础，采用学习控制的直接逆模型法来建立驾驶员模型，用此模型代替驾驶员或常规的PID模型进行联邦测试循环(Federal test procedure，FTP)标准工况的实验室测试。另一方面，为方便起见，建立并采用基于神经网络和汽车测试数据(Vehicle test data，VTD)的汽车模型来代替真实汽车进行模拟测试。以VTD以及FTP标准为背景的测试结果验证了所提方法的有效性。同时，采用个性化驾驶员模型能够在体现驾驶风格情况下完成标准工况测试，减轻劳动强度，并使实验室测试更接近于真实。
위료재차량측시중융입불동적가사풍격이사측시결과경접근우진실，제출일충기우기차측시수거화신경망락적가사원건모방법。고필도실제수거적고도리산성화국부돌변성，이국부성신경망락적전형대표소뇌모형관절공제기(Cerebellar model articulation controller，CMAC)망락위기출，채용학습공제적직접역모형법래건립가사원모형，용차모형대체가사원혹상규적PID모형진행련방측시순배(Federal test procedure，FTP)표준공황적실험실측시。령일방면，위방편기견，건립병채용기우신경망락화기차측시수거(Vehicle test data，VTD)적기차모형래대체진실기차진행모의측시。이VTD이급FTP표준위배경적측시결과험증료소제방법적유효성。동시，채용개성화가사원모형능구재체현가사풍격정황하완성표준공황측시，감경노동강도，병사실험실측시경접근우진실。
In order to integrate different driving styles into the vehicle test system and make the results more close to the reality, a novel method for driver modeling based on real-world road test data and neural network is proposed. Considering the divergence and local mutability of the real-world data, the cerebellar model articulation controller(CMAC), a locally designed neural network model, is utilized together with the direct inverse model approach to accomplish this model, which is used to replace the real driver or PID-like model for laboratory federal test procedure(FTP) test. On the other hand, a vehicle test data(VTD) and neural network based vehicle model is established and employed for simulation test. VTD and FTP based test are conducted to verify the effectiveness of the proposed scheme. Meanwhile, the personalized driver model is able to not only complete the standard vehicle test, but also mitigate the fatigue during driving.