重载货车驾驶人的激进驾驶风格具有强烈的习惯性特征和风险性特征,一旦养成很难矫正,且极易诱发交通事故。针对现有研究极少关注重载货车驾驶人驾驶风格的不足,本文基于某全国货运监管平台提供的云南省重载货车低频轨迹数据,从风格聚类...重载货车驾驶人的激进驾驶风格具有强烈的习惯性特征和风险性特征,一旦养成很难矫正,且极易诱发交通事故。针对现有研究极少关注重载货车驾驶人驾驶风格的不足,本文基于某全国货运监管平台提供的云南省重载货车低频轨迹数据,从风格聚类、风格识别和风格评估这3个方面,提出综合考虑疲劳驾驶特征和超速驾驶特征的重载货车驾驶人驾驶风格分析方法。首先,基于轨迹数据蕴含驾驶人驾驶行为模式的特点,构建表征重载货车驾驶人驾驶风格的疲劳驾驶和超速驾驶特征集;其次,利用因子分析进行特征约简,并采用K-均值聚类方法划分重载货车驾驶人的驾驶风格;然后,构建基于支持向量机的驾驶风格识别模型,并与梯度提升决策树的识别结果进行对比;最后,基于疲劳驾驶特征和超速驾驶特征的累积分布,建立基于CRITIC(Criteria Importance Though Intercriteria Correlation)赋权法的重载货车驾驶人驾驶风格量化评估模型。研究结果表明:经过特征约简,提取的疲劳因子和超速因子能综合反映上述两类特征集80.838%的信息;根据疲劳因子和超速因子可将驾驶风格划分为4种类别,即稳健型、超速型、疲劳型和危险型,相应重载货车驾驶人比例依次为62.60%、25.02%、7.40%和4.98%;基于支持向量机的重载货车驾驶人驾驶风格识别模型对不同风格的识别准确率均大于97%,整体表现优于梯度提升决策树;基于CRITIC赋权法的驾驶风格评估模型能有效量化重载货车驾驶人的驾驶风格,其中稳健型驾驶人表现最好,75%以上的驾驶人风格评估总分高于60分;危险型驾驶人表现最差,75%以上的驾驶人风格评估总分低于20分。研究结果可为重载货车驾驶人不良驾驶行为的监测、干预和管理提供理论依据和技术支撑。展开更多
Fuel consumption is one of the main concerns for heavy-duty trucks.Predictive cruise control(PCC)provides an intriguing opportunity to reduce fuel consumption by using the upcoming road information.In this study,a rea...Fuel consumption is one of the main concerns for heavy-duty trucks.Predictive cruise control(PCC)provides an intriguing opportunity to reduce fuel consumption by using the upcoming road information.In this study,a real-time implementable PCC,which simultaneously optimizes engine torque and gear shifting,is proposed for heavy-duty trucks.To minimize fuel consumption,the problem of the PCC is formulated as a nonlinear model predictive control(MPC),in which the upcoming road elevation information is used.Finding the solution of the nonlinear MPC is time consuming;thus,a real-time implementable solver is developed based on Pontryagin’s maximum principle and indirect shooting method.Dynamic programming(DP)algorithm,as a global optimization algorithm,is used as a performance benchmark for the proposed solver.Simulation,hardware-in-the-loop and real-truck experiments are conducted to verify the performance of the proposed controller.The results demonstrate that the MPC-based solution performs nearly as well as the DP-based solution,with less than 1%deviation for testing roads.Moreover,the proposed co-optimization controller is implementable in a real-truck,and the proposed MPC-based PCC algorithm achieves a fuel-saving rate of 7.9%without compromising the truck’s travel time.展开更多
文摘重载货车驾驶人的激进驾驶风格具有强烈的习惯性特征和风险性特征,一旦养成很难矫正,且极易诱发交通事故。针对现有研究极少关注重载货车驾驶人驾驶风格的不足,本文基于某全国货运监管平台提供的云南省重载货车低频轨迹数据,从风格聚类、风格识别和风格评估这3个方面,提出综合考虑疲劳驾驶特征和超速驾驶特征的重载货车驾驶人驾驶风格分析方法。首先,基于轨迹数据蕴含驾驶人驾驶行为模式的特点,构建表征重载货车驾驶人驾驶风格的疲劳驾驶和超速驾驶特征集;其次,利用因子分析进行特征约简,并采用K-均值聚类方法划分重载货车驾驶人的驾驶风格;然后,构建基于支持向量机的驾驶风格识别模型,并与梯度提升决策树的识别结果进行对比;最后,基于疲劳驾驶特征和超速驾驶特征的累积分布,建立基于CRITIC(Criteria Importance Though Intercriteria Correlation)赋权法的重载货车驾驶人驾驶风格量化评估模型。研究结果表明:经过特征约简,提取的疲劳因子和超速因子能综合反映上述两类特征集80.838%的信息;根据疲劳因子和超速因子可将驾驶风格划分为4种类别,即稳健型、超速型、疲劳型和危险型,相应重载货车驾驶人比例依次为62.60%、25.02%、7.40%和4.98%;基于支持向量机的重载货车驾驶人驾驶风格识别模型对不同风格的识别准确率均大于97%,整体表现优于梯度提升决策树;基于CRITIC赋权法的驾驶风格评估模型能有效量化重载货车驾驶人的驾驶风格,其中稳健型驾驶人表现最好,75%以上的驾驶人风格评估总分高于60分;危险型驾驶人表现最差,75%以上的驾驶人风格评估总分低于20分。研究结果可为重载货车驾驶人不良驾驶行为的监测、干预和管理提供理论依据和技术支撑。
基金Supported by International Technology Cooperation Program of Science and Technology Commission of Shanghai Municipality of China(Grant No.21160710600)National Nature Science Foundation of China(Grant No.52372393)Shanghai Pujiang Program of China(Grant No.21PJD075).
文摘Fuel consumption is one of the main concerns for heavy-duty trucks.Predictive cruise control(PCC)provides an intriguing opportunity to reduce fuel consumption by using the upcoming road information.In this study,a real-time implementable PCC,which simultaneously optimizes engine torque and gear shifting,is proposed for heavy-duty trucks.To minimize fuel consumption,the problem of the PCC is formulated as a nonlinear model predictive control(MPC),in which the upcoming road elevation information is used.Finding the solution of the nonlinear MPC is time consuming;thus,a real-time implementable solver is developed based on Pontryagin’s maximum principle and indirect shooting method.Dynamic programming(DP)algorithm,as a global optimization algorithm,is used as a performance benchmark for the proposed solver.Simulation,hardware-in-the-loop and real-truck experiments are conducted to verify the performance of the proposed controller.The results demonstrate that the MPC-based solution performs nearly as well as the DP-based solution,with less than 1%deviation for testing roads.Moreover,the proposed co-optimization controller is implementable in a real-truck,and the proposed MPC-based PCC algorithm achieves a fuel-saving rate of 7.9%without compromising the truck’s travel time.
