This paper gives an overview of studies on parameters displayed on the Automotive Head Up Display （A-HUD） including calculation and construction of symbology page based on study results. A study has been made on vit...This paper gives an overview of studies on parameters displayed on the Automotive Head Up Display （A-HUD） including calculation and construction of symbology page based on study results. A study has been made on vital parameters required for car drivers and design calculations have been made based on design parameters like field of view, distance from the design eye position, minimum character size viewable from a distance of 1.5m between driver and the projected image, and optical magnification factor. lhe display format suitable for A-HUD applications depends upon the parameters required to be displayed. The aspect ratio chosen is 4：3. This paper also provides method to design the symbology page embedding six vital parameters with their relative positioning and size considering relative position between display device and optical elements which has been considered with a magnification factor of 2.5. The field of view obtained is 6.7° × 4.8°.展开更多
Digital display instrument identification is a crucial approach for automating the collection of digital display data.In this study,we propose a digital display area detection CTPNpro algorithm to address the problem ...Digital display instrument identification is a crucial approach for automating the collection of digital display data.In this study,we propose a digital display area detection CTPNpro algorithm to address the problem of recognizing multiclass digital display instruments.We developed a multiclass digital display instrument recognition algorithm by combining the character recognition network constructed using a convolutional neural network and bidirectional variable-length long short-term memory(LSTM).First,the digital display region detection CTPNpro network framework was designed based on the CTPN network architecture by introducing feature fusion and residual structure.Next,the digital display instrument identification network was constructed based on a convolutional neural network using twoway LSTM and Connectionist temporal classification(CTC)of indefinite length.Finally,an automatic calibration system for digital display instruments was built,and a multiclass digital display instrument dataset was constructed by sampling in the system.We compared the performance of the CTPNpro algorithm with other methods using this dataset to validate the effectiveness and robustness of the proposed algorithm.展开更多
文摘This paper gives an overview of studies on parameters displayed on the Automotive Head Up Display （A-HUD） including calculation and construction of symbology page based on study results. A study has been made on vital parameters required for car drivers and design calculations have been made based on design parameters like field of view, distance from the design eye position, minimum character size viewable from a distance of 1.5m between driver and the projected image, and optical magnification factor. lhe display format suitable for A-HUD applications depends upon the parameters required to be displayed. The aspect ratio chosen is 4：3. This paper also provides method to design the symbology page embedding six vital parameters with their relative positioning and size considering relative position between display device and optical elements which has been considered with a magnification factor of 2.5. The field of view obtained is 6.7° × 4.8°.
基金supported by the National Key R&D Program of China(2022YFB4701502)the“Leading Goose”R&D Program of Zhejiang(2023C01177)+1 种基金the Key Research Project of Zhejiang Lab(2021NB0AL03)the Key R&D Project on Agriculture and Social Development in Hangzhou City(Asian Games)(20230701 A05).
文摘Digital display instrument identification is a crucial approach for automating the collection of digital display data.In this study,we propose a digital display area detection CTPNpro algorithm to address the problem of recognizing multiclass digital display instruments.We developed a multiclass digital display instrument recognition algorithm by combining the character recognition network constructed using a convolutional neural network and bidirectional variable-length long short-term memory(LSTM).First,the digital display region detection CTPNpro network framework was designed based on the CTPN network architecture by introducing feature fusion and residual structure.Next,the digital display instrument identification network was constructed based on a convolutional neural network using twoway LSTM and Connectionist temporal classification(CTC)of indefinite length.Finally,an automatic calibration system for digital display instruments was built,and a multiclass digital display instrument dataset was constructed by sampling in the system.We compared the performance of the CTPNpro algorithm with other methods using this dataset to validate the effectiveness and robustness of the proposed algorithm.
