Srivastava and Jhajj [ 1 6] proposed a class of estimators for estimating population variance using multi auxiliary variables in simple random sampling and they utilized the means and variances of auxiliary variables....Srivastava and Jhajj [ 1 6] proposed a class of estimators for estimating population variance using multi auxiliary variables in simple random sampling and they utilized the means and variances of auxiliary variables. In this paper, we adapted this class and motivated by Searle [13], and we suggested more generalized class of estimators for estimating the population variance in simple random sampling. The expressions for the mean square error of proposed class have been derived in general form. Besides obtaining the minimized MSE of the proposed and adapted class, it is shown that the adapted classis the special case of the proposed class. Moreover, these theoretical findings are supported by an empirical study of original data.展开更多
End-to-end self-driving is a method that directly maps raw visual images to vehicle control signals using deep convolutional neural network(CNN).Although prediction of steering angle has achieved good result in single...End-to-end self-driving is a method that directly maps raw visual images to vehicle control signals using deep convolutional neural network(CNN).Although prediction of steering angle has achieved good result in single task,the current approach does not effectively simultaneously predict the steering angle and the speed.In this paper,various end-to-end multi-task deep learning networks using deep convolutional neural network combined with long short-term memory recurrent neural network(CNN-LSTM)are designed and compared,which could obtain not only the visual spatial information but also the dynamic temporal information in the driving scenarios,and improve steering angle and speed predictions.Furthermore,two auxiliary tasks based on semantic segmentation and object detection are proposed to improve the understanding of driving scenarios.Experiments are conducted on the public Udacity dataset and a newly collected Guangzhou Automotive Cooperate dataset.The results show that the proposed network architecture could predict steering angles and vehicle speed accurately.In addi-tion,the impact of multi-auxiliary tasks on the network performance is analyzed by visualization method,which shows the salient map of network.Finally,the proposed network architecture has been well verified on the autonomous driving simu-lation platform Grand Theft Auto V(GTAV)and experimental road with an average takeover rate of two times per 10 km.展开更多
文摘Srivastava and Jhajj [ 1 6] proposed a class of estimators for estimating population variance using multi auxiliary variables in simple random sampling and they utilized the means and variances of auxiliary variables. In this paper, we adapted this class and motivated by Searle [13], and we suggested more generalized class of estimators for estimating the population variance in simple random sampling. The expressions for the mean square error of proposed class have been derived in general form. Besides obtaining the minimized MSE of the proposed and adapted class, it is shown that the adapted classis the special case of the proposed class. Moreover, these theoretical findings are supported by an empirical study of original data.
基金This work is funded by the Youth Talent Lifting Project of China Society of Automotive Engineers.
文摘End-to-end self-driving is a method that directly maps raw visual images to vehicle control signals using deep convolutional neural network(CNN).Although prediction of steering angle has achieved good result in single task,the current approach does not effectively simultaneously predict the steering angle and the speed.In this paper,various end-to-end multi-task deep learning networks using deep convolutional neural network combined with long short-term memory recurrent neural network(CNN-LSTM)are designed and compared,which could obtain not only the visual spatial information but also the dynamic temporal information in the driving scenarios,and improve steering angle and speed predictions.Furthermore,two auxiliary tasks based on semantic segmentation and object detection are proposed to improve the understanding of driving scenarios.Experiments are conducted on the public Udacity dataset and a newly collected Guangzhou Automotive Cooperate dataset.The results show that the proposed network architecture could predict steering angles and vehicle speed accurately.In addi-tion,the impact of multi-auxiliary tasks on the network performance is analyzed by visualization method,which shows the salient map of network.Finally,the proposed network architecture has been well verified on the autonomous driving simu-lation platform Grand Theft Auto V(GTAV)and experimental road with an average takeover rate of two times per 10 km.
