This study presents a general optimal trajectory planning(GOTP)framework for autonomous vehicles(AVs)that can effectively avoid obstacles and guide AVs to complete driving tasks safely and efficiently.Firstly,we emplo...This study presents a general optimal trajectory planning(GOTP)framework for autonomous vehicles(AVs)that can effectively avoid obstacles and guide AVs to complete driving tasks safely and efficiently.Firstly,we employ the fifth-order Bezier curve to generate and smooth the reference path along the road centerline.Cartesian coordinates are then transformed to achieve the curvature continuity of the generated curve.Considering the road constraints and vehicle dynamics,limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate system.Furthermore,in selecting the optimal trajectory,we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers’behavior and summarizing their manipulation characteristics of“seeking benefits and avoiding losses.”Finally,by integrating the idea of receding-horizon optimization,the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility,optimality,and adaptability.Extensive simulations and experiments are performed,and the results demonstrate the framework’s feasibility and effectiveness,which avoids both dynamic and static obstacles and applies to various scenarios with multi-source interactive traffic participants.Moreover,we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers’manipulation.展开更多
Maintaining software once implemented on the end-user side is laborious and,over its lifetime,is most often considerably more expensive than the initial software development.The prediction of software maintainability ...Maintaining software once implemented on the end-user side is laborious and,over its lifetime,is most often considerably more expensive than the initial software development.The prediction of software maintainability lias emerged as an important research topic to address industry expectations for reducing costs,in particular,maintenance costs.Researchers and practitioners have been working on proposing and identifying a variety of techniques ranging from statistical to machine learning(ML)for better prediction of software maintainability.This review has been carried out to analyze the empirical evidence on the accuracy of software product maintainability prediction(SPMP)using ML techniques.This paper analyzes and discusses the findings of 77 selected studies published from 2000 to 2018 according to the following criteria:maintainability prediction techniques,validation methods,accuracy criteria,overall accuracy of ML techniques,and the techniques offering the best performance.The review process followed the well-known syslematic review process.The results show that ML techniques are frequently used in predicting maintainability.In particular,artificial neural network(ANN),support vector machine/regression(SVM/R).regression&decision trees(DT),and fuzzy neuro fuzzy(FNF)techniques are more accurate in terms of PRED and MMRE.The N-fold and leave-one-out cross-validation methods,and the MMRE and PRED accuracy criteria are frequently used in empirical studies.In general,ML techniques outperformed non-machine learning techniques,e.g.,regression analysis(RA)techniques,while FNF outperformed SVM/R.DT.and ANN in most experiments.However,while many techniques were reported superior,no specific one can be identified as the best.展开更多
基金supported by the National Natural Science Foundation of China(the Key Project,52131201Science Fund for Creative Research Groups,52221005)+1 种基金the China Scholarship Councilthe Joint Laboratory for Internet of Vehicles,Ministry of Education–China MOBILE Communications Corporation。
文摘This study presents a general optimal trajectory planning(GOTP)framework for autonomous vehicles(AVs)that can effectively avoid obstacles and guide AVs to complete driving tasks safely and efficiently.Firstly,we employ the fifth-order Bezier curve to generate and smooth the reference path along the road centerline.Cartesian coordinates are then transformed to achieve the curvature continuity of the generated curve.Considering the road constraints and vehicle dynamics,limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate system.Furthermore,in selecting the optimal trajectory,we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers’behavior and summarizing their manipulation characteristics of“seeking benefits and avoiding losses.”Finally,by integrating the idea of receding-horizon optimization,the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility,optimality,and adaptability.Extensive simulations and experiments are performed,and the results demonstrate the framework’s feasibility and effectiveness,which avoids both dynamic and static obstacles and applies to various scenarios with multi-source interactive traffic participants.Moreover,we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers’manipulation.
文摘Maintaining software once implemented on the end-user side is laborious and,over its lifetime,is most often considerably more expensive than the initial software development.The prediction of software maintainability lias emerged as an important research topic to address industry expectations for reducing costs,in particular,maintenance costs.Researchers and practitioners have been working on proposing and identifying a variety of techniques ranging from statistical to machine learning(ML)for better prediction of software maintainability.This review has been carried out to analyze the empirical evidence on the accuracy of software product maintainability prediction(SPMP)using ML techniques.This paper analyzes and discusses the findings of 77 selected studies published from 2000 to 2018 according to the following criteria:maintainability prediction techniques,validation methods,accuracy criteria,overall accuracy of ML techniques,and the techniques offering the best performance.The review process followed the well-known syslematic review process.The results show that ML techniques are frequently used in predicting maintainability.In particular,artificial neural network(ANN),support vector machine/regression(SVM/R).regression&decision trees(DT),and fuzzy neuro fuzzy(FNF)techniques are more accurate in terms of PRED and MMRE.The N-fold and leave-one-out cross-validation methods,and the MMRE and PRED accuracy criteria are frequently used in empirical studies.In general,ML techniques outperformed non-machine learning techniques,e.g.,regression analysis(RA)techniques,while FNF outperformed SVM/R.DT.and ANN in most experiments.However,while many techniques were reported superior,no specific one can be identified as the best.
