With the rapid growth of global air traffic,flight delays are increasingly serious.Convective weather is one of the influential causes for flight delays,which has affected the sustainable development of civil aviation...With the rapid growth of global air traffic,flight delays are increasingly serious.Convective weather is one of the influential causes for flight delays,which has affected the sustainable development of civil aviation industry and became a social problem.If it can be predicted that whether a weather-related flight diverts,participants in air traffic activities can coordinate the scheduling,and flight delays can be reduced greatly.In this paper,the weather avoidance prediction model(WAPM)is proposed to find the relationship between weather and flight trajectories,and predict whether a future flight diverts based on historical flight data.First,given the large amount of weather data,the principal component analysis is used to reduce the ten dimensional weather indicators to extract 90%information.Second,the support vector machine is adopted to predict whether the flight diverts by determining the hyperparameters c and γ of the radial basis function.Finally,the performance of the proposed model is evaluated by prediction accuracy,precision,recall and F1,and compared with the methods of the k nearest neighbor(kNN),the logistic regression(LR),the random forest(RF)and the deep neural networks(DNNs).WAPM’s accuracy is 5.22%,2.63%,2.26%and 1.03%greater than those of kNN,LR,RF and DNNs,respectively;WAPM’s precision is 6.79%,5.19%,4.37%and 3.21%greater than those of kNN,LR,RF and DNNs,respectively;WAPM’s recall is 4.05%,1.05%,0.04%greater than those of kNN,LR,and RF,respectively,and 1.38%lower than that of the DNNs;and F1 of WAPM is 5.28%,1.69%,1.98%and 0.68%greater than those of kNN,LR,RF and DNNs,respectively.展开更多
For autonomous vehicles and driver assist systems,path planning and collision avoidance algorithms benefit from accurate predictions of future location of other vehicles and intent of their drivers.In the literature,t...For autonomous vehicles and driver assist systems,path planning and collision avoidance algorithms benefit from accurate predictions of future location of other vehicles and intent of their drivers.In the literature,the algorithms that provide driver intent belong to two categories:those that use physics based models with some type of filtering,and machine learning based approaches.In this paper we employ barrier functions(BF)to decide driver intent.BFs are typically used to prove safety by establishing forward invariance of an admissible set.Here,we decide if the“target”vehicle is violating one or more possibly fictitious(i.e.,non-physical)barrier constraints determined based on the context provided by the road geometry.The algorithm has a very small computational footprint and better false positive and negative rates than some of the alternatives.The predicted intent is then used by a control barrier function(CBF)based collision avoidance system to prevent unnecessary interventions,for either an autonomous or human-driven vehicle.展开更多
In order to overcome the shortcomings of the previous obstacle avoidance algorithms,an obstacle avoidance algorithm applicable to multiple mobile obstacles was proposed.The minimum prediction distance between obstacle...In order to overcome the shortcomings of the previous obstacle avoidance algorithms,an obstacle avoidance algorithm applicable to multiple mobile obstacles was proposed.The minimum prediction distance between obstacles and a manipulator was obtained according to the states of obstacles and transformed to escape velocity of the corresponding link of the manipulator.The escape velocity was introduced to the gradient projection method to obtain the joint velocity of the manipulator so as to complete the obstacle avoidance trajectory planning.A7-DOF manipulator was used in the simulation,and the results verified the effectiveness of the algorithm.展开更多
基金supported by Nanjing University of Aeronautics and Astronautics Graduate Innovation Base(Laboratory)Open Fund(No.kfjj20200710).
文摘With the rapid growth of global air traffic,flight delays are increasingly serious.Convective weather is one of the influential causes for flight delays,which has affected the sustainable development of civil aviation industry and became a social problem.If it can be predicted that whether a weather-related flight diverts,participants in air traffic activities can coordinate the scheduling,and flight delays can be reduced greatly.In this paper,the weather avoidance prediction model(WAPM)is proposed to find the relationship between weather and flight trajectories,and predict whether a future flight diverts based on historical flight data.First,given the large amount of weather data,the principal component analysis is used to reduce the ten dimensional weather indicators to extract 90%information.Second,the support vector machine is adopted to predict whether the flight diverts by determining the hyperparameters c and γ of the radial basis function.Finally,the performance of the proposed model is evaluated by prediction accuracy,precision,recall and F1,and compared with the methods of the k nearest neighbor(kNN),the logistic regression(LR),the random forest(RF)and the deep neural networks(DNNs).WAPM’s accuracy is 5.22%,2.63%,2.26%and 1.03%greater than those of kNN,LR,RF and DNNs,respectively;WAPM’s precision is 6.79%,5.19%,4.37%and 3.21%greater than those of kNN,LR,RF and DNNs,respectively;WAPM’s recall is 4.05%,1.05%,0.04%greater than those of kNN,LR,and RF,respectively,and 1.38%lower than that of the DNNs;and F1 of WAPM is 5.28%,1.69%,1.98%and 0.68%greater than those of kNN,LR,RF and DNNs,respectively.
文摘For autonomous vehicles and driver assist systems,path planning and collision avoidance algorithms benefit from accurate predictions of future location of other vehicles and intent of their drivers.In the literature,the algorithms that provide driver intent belong to two categories:those that use physics based models with some type of filtering,and machine learning based approaches.In this paper we employ barrier functions(BF)to decide driver intent.BFs are typically used to prove safety by establishing forward invariance of an admissible set.Here,we decide if the“target”vehicle is violating one or more possibly fictitious(i.e.,non-physical)barrier constraints determined based on the context provided by the road geometry.The algorithm has a very small computational footprint and better false positive and negative rates than some of the alternatives.The predicted intent is then used by a control barrier function(CBF)based collision avoidance system to prevent unnecessary interventions,for either an autonomous or human-driven vehicle.
基金Supported by Ministeral Level Advanced Research Foundation(65822576)Beijing Municipal Education Commission(KM201310858004,KM201310858001)
文摘In order to overcome the shortcomings of the previous obstacle avoidance algorithms,an obstacle avoidance algorithm applicable to multiple mobile obstacles was proposed.The minimum prediction distance between obstacles and a manipulator was obtained according to the states of obstacles and transformed to escape velocity of the corresponding link of the manipulator.The escape velocity was introduced to the gradient projection method to obtain the joint velocity of the manipulator so as to complete the obstacle avoidance trajectory planning.A7-DOF manipulator was used in the simulation,and the results verified the effectiveness of the algorithm.
