Driving safety field(DSF) model has been proposed to represent comprehensive driving risk formed by interactions of driver-vehicle-road in mixed traffic environment. In this work, we establish an optimization model ba...Driving safety field(DSF) model has been proposed to represent comprehensive driving risk formed by interactions of driver-vehicle-road in mixed traffic environment. In this work, we establish an optimization model based on grey relation degree analysis to calibrate risk coefficients of DSF model. To solve the optimum solution, a genetic algorithm is employed. Finally, the DSF model is verified through a real-world driving experiment. Results show that the DSF model is consistent with driver's hazard perception and more sensitive than TTC. Moreover, the proposed DSF model offers a novel way for criticality assessment and decision-making of advanced driver assistance systems and intelligent connected vehicles.展开更多
The main principle and mathematical model of GOCE kinematic orbit adjustment for Earth gravity field model （EGM） validation and accelerometer calibration are presented. Based on 60 days GOCE kinematic orbits with 1-...The main principle and mathematical model of GOCE kinematic orbit adjustment for Earth gravity field model （EGM） validation and accelerometer calibration are presented. Based on 60 days GOCE kinematic orbits with 1-2 cm accuracy and accelerometer data from 2009-11-02 to 2009-12-31, the RMS-of-fit （ROF） of them using EGM2008, EIGEN-SC, ITG- GRACE2010S and GOCO01S up to 120, 150 and 180 degree and order （d/o） are evaluated and compared. The scale factors and biases of GOCE accelerometer data are calibrated and the energy balance method （EBM） is performed to test the accuracy of accelerometer calibration. The results show that GOCE orbits are also sensitive to EGM from 120 to 150 d/o. The ROFs of EGMs with 150 and 180 d/o are obviously better than those of EGMs with 120 d/o. The ROFs of GOCO01S and ITG-GRACE2010S are almost the same up to 120 and 150 d/o, which are about 3.3 cm and 1.8 cm, respectively. They are far better than those of EGM2008 and EIGEN-SC with the same d/o. The ROF of GOCO01S with 180 d/o is about 1.6 em, which is the best one among those EGMs. The accelerometer calibration accuracies （ACAs） of ITG-GRACE2010S and GOCO01S are obviously higher that those of EGM2008 and EIGEN-SC. The ACA of GOCO01S with 180 d/o is far higher than that of EGMs with 120 d/o, and a little higher than that of ITG-GRACE2010S with 150 d/o. I t is suggested that the newest released EGM such as GOCO01S or GOCO02S till at least 150 d/o should be chosen in GOCE precise orbit determination （POD） and accelerometer calibration.展开更多
基金Projects(51475254,51625503)supported by the National Natural Science Foundation of ChinaProject(MCM20150302)supported by the Joint Project of Tsinghua and China Mobile,ChinaProject supported by the joint Project of Tsinghua and Daimler Greater China Ltd.,Beijing,China
文摘Driving safety field(DSF) model has been proposed to represent comprehensive driving risk formed by interactions of driver-vehicle-road in mixed traffic environment. In this work, we establish an optimization model based on grey relation degree analysis to calibrate risk coefficients of DSF model. To solve the optimum solution, a genetic algorithm is employed. Finally, the DSF model is verified through a real-world driving experiment. Results show that the DSF model is consistent with driver's hazard perception and more sensitive than TTC. Moreover, the proposed DSF model offers a novel way for criticality assessment and decision-making of advanced driver assistance systems and intelligent connected vehicles.
基金Project(41174008)supported by the National Natural Science Foundation of ChinaProject(SKLGED2013-4-2-EZ)supported by the Open Foundation of State Key Laboratory of Geodesy and Earth’s Dynamics,ChinaProject(2007B51)supported by the Foundation for the Author of National Excellent Doctoral Dissertation of China
文摘The main principle and mathematical model of GOCE kinematic orbit adjustment for Earth gravity field model （EGM） validation and accelerometer calibration are presented. Based on 60 days GOCE kinematic orbits with 1-2 cm accuracy and accelerometer data from 2009-11-02 to 2009-12-31, the RMS-of-fit （ROF） of them using EGM2008, EIGEN-SC, ITG- GRACE2010S and GOCO01S up to 120, 150 and 180 degree and order （d/o） are evaluated and compared. The scale factors and biases of GOCE accelerometer data are calibrated and the energy balance method （EBM） is performed to test the accuracy of accelerometer calibration. The results show that GOCE orbits are also sensitive to EGM from 120 to 150 d/o. The ROFs of EGMs with 150 and 180 d/o are obviously better than those of EGMs with 120 d/o. The ROFs of GOCO01S and ITG-GRACE2010S are almost the same up to 120 and 150 d/o, which are about 3.3 cm and 1.8 cm, respectively. They are far better than those of EGM2008 and EIGEN-SC with the same d/o. The ROF of GOCO01S with 180 d/o is about 1.6 em, which is the best one among those EGMs. The accelerometer calibration accuracies （ACAs） of ITG-GRACE2010S and GOCO01S are obviously higher that those of EGM2008 and EIGEN-SC. The ACA of GOCO01S with 180 d/o is far higher than that of EGMs with 120 d/o, and a little higher than that of ITG-GRACE2010S with 150 d/o. I t is suggested that the newest released EGM such as GOCO01S or GOCO02S till at least 150 d/o should be chosen in GOCE precise orbit determination （POD） and accelerometer calibration.
文摘蒸散发(Evapotranspiration,ET)是作物需水量的核心组分,也是区域水资源优化配置的关键依据。本文以陕西关中宝鸡峡灌区夏玉米为研究对象,采用BP神经网络(Back propagation neural network,BPNN)、支持向量机(Support vector machine,SVM)、极限学习机(Extreme learning machine,ELM)和极致梯度提升树(eXtreme gradient boosting,XGBoost)4种机器学习算法构建无人机-卫星多源遥感数据协同校正模型,并以最优算法建立的模型校正卫星多光谱数据,实现无人机和卫星数据的尺度转换。利用校正后高精度卫星数据反演夏玉米叶面积指数(Leaf area index,LAI)与株高(Crop height,hc)为蒸散发模型提供数据输入。分别采用双作物系数法、METRIC模型及Penman-Monteith(P-M)冠层阻力模型进行夏玉米蒸散发估算,引入贝叶斯模型平均(Bayesian model averaging,BMA)实现不同生育阶段各方法/模型权重的动态分配,最终得到玉米拔节-完熟期性能稳健的蒸散发BMA融合模型。结果表明:XGBoost算法在夏玉米拔节-完熟期的B/G/R/NIR波段建模精度均为最高,四波段建模结果决定系数(Coefficient of determination,R^(2))较算法ELM高出8.43%、8.67%、6.79%和10.41%;校正后的卫星多光谱数据LAI与hc反演结果R^(2)较原始卫星数据分别平均提高97%和67.5%;BMA融合模型在夏玉米拔节-抽雄期和蜡熟-完熟期较单一最优方法/模型(METRIC模型)均方根误差(Root mean squared error,RMSE)降低39.3%~58.5%。本研究利用“协同校正-动态融合”显著提升了蒸散发遥感监测精度,可为水资源精细化管理提供理论支撑。
