A new vision-based long-distance lane perception and front vehicle location method was developed for decision making of full autonomous vehicles on highway roads,Firstly,a real-time long-distance lane detection approa...A new vision-based long-distance lane perception and front vehicle location method was developed for decision making of full autonomous vehicles on highway roads,Firstly,a real-time long-distance lane detection approach was presented based on a linear-cubic road model for two-lane highways.By using a novel robust lane marking feature which combines the constraints of intensity,edge and width,the lane markings in far regions were extracted accurately and efficiently.Next,the detected lane lines were selected and tracked by estimating the lateral offset and heading angle of ego vehicle with a Kalman filter,Finally,front vehicles were located on correct lanes using the tracked lane lines,Experiment results show that the proposed lane perception approach can achieve an average correct detection rate of 94.37% with an average false positive detection rate of 0.35%,The proposed approaches for long-distance lane perception and front vehicle location were validated in a 286 km full autonomous drive experiment under real traffic conditions.This successful experiment shows that the approaches are effective and robust enough for full autonomous vehicles on highway roads.展开更多
Machine learning models are constructed to predict fragment production cross sections in projectile fragmentation(PF)reactions using Bayesian neural network(BNN)techniques.The massive learning for BNN models is based ...Machine learning models are constructed to predict fragment production cross sections in projectile fragmentation(PF)reactions using Bayesian neural network(BNN)techniques.The massive learning for BNN models is based on 6393 fragments from 53 measured projectile fragmentation reactions.A direct BNN model and physical guiding BNN via FRACS parametrization(BNN+FRACS)model have been constructed to predict the fragment cross section in projectile fragmentation reactions.It is verified that the BNN and BNN+FRACS models can reproduce a wide range of fragment productions in PF reactions with incident energies from 40 MeV/u to 1 GeV/u,reaction systems with projectile nuclei from^40 Ar to^208 Pb,and various target nuclei.The high precision of the BNN and BNN+FRACS models makes them applicable for the low production rate of extremely rare isotopes in future PF reactions with large projectile nucleus asymmetry in the new generation of radioactive nuclear beam factories.展开更多
基金Project(90820302) supported by the National Natural Science Foundation of China
文摘A new vision-based long-distance lane perception and front vehicle location method was developed for decision making of full autonomous vehicles on highway roads,Firstly,a real-time long-distance lane detection approach was presented based on a linear-cubic road model for two-lane highways.By using a novel robust lane marking feature which combines the constraints of intensity,edge and width,the lane markings in far regions were extracted accurately and efficiently.Next,the detected lane lines were selected and tracked by estimating the lateral offset and heading angle of ego vehicle with a Kalman filter,Finally,front vehicles were located on correct lanes using the tracked lane lines,Experiment results show that the proposed lane perception approach can achieve an average correct detection rate of 94.37% with an average false positive detection rate of 0.35%,The proposed approaches for long-distance lane perception and front vehicle location were validated in a 286 km full autonomous drive experiment under real traffic conditions.This successful experiment shows that the approaches are effective and robust enough for full autonomous vehicles on highway roads.
基金Supported by the National Natural Science Foundation of China(11975091)the Program for Innovative Research Team(in Science and Technology)in University of Henan Province(21IRTSTHN011),China。
文摘Machine learning models are constructed to predict fragment production cross sections in projectile fragmentation(PF)reactions using Bayesian neural network(BNN)techniques.The massive learning for BNN models is based on 6393 fragments from 53 measured projectile fragmentation reactions.A direct BNN model and physical guiding BNN via FRACS parametrization(BNN+FRACS)model have been constructed to predict the fragment cross section in projectile fragmentation reactions.It is verified that the BNN and BNN+FRACS models can reproduce a wide range of fragment productions in PF reactions with incident energies from 40 MeV/u to 1 GeV/u,reaction systems with projectile nuclei from^40 Ar to^208 Pb,and various target nuclei.The high precision of the BNN and BNN+FRACS models makes them applicable for the low production rate of extremely rare isotopes in future PF reactions with large projectile nucleus asymmetry in the new generation of radioactive nuclear beam factories.
