In order to reduce the number of surface mining accidents related to low visibility conditions and blind spots of trucks and to provide 3D information for truck drivers and real time monitored truck information for th...In order to reduce the number of surface mining accidents related to low visibility conditions and blind spots of trucks and to provide 3D information for truck drivers and real time monitored truck information for the remote dispatcher, a 3D assisted driving system (3D-ADS) based on the GPS, mesh-wireless networks and the Google-Earth engine as the graphic interface and mine-mapping server, was developed at Virginia Tech. The research results indicate that this 3D-ADS system has the potential to increase reliability and reduce uncertainty in open pit mining operations by customizing the local 3D digital mining map, con-structing 3D truck models, tracking vehicles in real time using a 3D interface and indicating available escape routes for driver safety.展开更多
The authors propose a two-stage method for recognizing driving situations on the basis of driving signals for application to a safe human interface of an in-vehicle information system. In first stage, an unknown drivi...The authors propose a two-stage method for recognizing driving situations on the basis of driving signals for application to a safe human interface of an in-vehicle information system. In first stage, an unknown driving situation is determined as stopping behavior or non-stopping behavior. In second stage, a Hidden Markov Model （HMM）-based pattern recognition method is used to model and recognize six non-stopping driving situations. The authors attempt to find the optimal HMM configuration to improve the performance of driving situation recognition. Center for Integrated Acoustic Information Research （CLAIR） in-vehicle corpus is used to evaluate the HMM-based recognition method. Driving situation categories are recognized using five driving signals. The proposed method achieves a relative error reduction rate of 30.9% compared to a conventional one-stage based HMMs.展开更多
基金Financial support for this work, provided by the Key Programs of the National Science and Technology Foundation during the 11th Five-Year Plan Period (No.2006BAK04B04) the State Scholarship Fund (No.2007104096), is gratefully acknowledged
文摘In order to reduce the number of surface mining accidents related to low visibility conditions and blind spots of trucks and to provide 3D information for truck drivers and real time monitored truck information for the remote dispatcher, a 3D assisted driving system (3D-ADS) based on the GPS, mesh-wireless networks and the Google-Earth engine as the graphic interface and mine-mapping server, was developed at Virginia Tech. The research results indicate that this 3D-ADS system has the potential to increase reliability and reduce uncertainty in open pit mining operations by customizing the local 3D digital mining map, con-structing 3D truck models, tracking vehicles in real time using a 3D interface and indicating available escape routes for driver safety.
文摘The authors propose a two-stage method for recognizing driving situations on the basis of driving signals for application to a safe human interface of an in-vehicle information system. In first stage, an unknown driving situation is determined as stopping behavior or non-stopping behavior. In second stage, a Hidden Markov Model （HMM）-based pattern recognition method is used to model and recognize six non-stopping driving situations. The authors attempt to find the optimal HMM configuration to improve the performance of driving situation recognition. Center for Integrated Acoustic Information Research （CLAIR） in-vehicle corpus is used to evaluate the HMM-based recognition method. Driving situation categories are recognized using five driving signals. The proposed method achieves a relative error reduction rate of 30.9% compared to a conventional one-stage based HMMs.
