The lane changing decision model(LCDM)is a critical component in semi-and fully-automated driving systems.Recent research has found that the fuzzy inference system(FIS)is a promising approach to implementing LCDMs.To ...The lane changing decision model(LCDM)is a critical component in semi-and fully-automated driving systems.Recent research has found that the fuzzy inference system(FIS)is a promising approach to implementing LCDMs.To improve the FIS’s performance,this research reviewed the challenges in the development an FIS model to make the yes;nof g decisions in discretionary lane changes.The FIS model was revised to bring its fuzzy inference rules more consistent with the fuzzy membership functions,and its com-position and defuzzification methods more in line with the classical fuzzy logic theory.An equitable test data set with approximately equal number of yes;nof g data points was assembled from the same next generation simulation(NGSIM)data used in the past research.The test results proved that:(1)an LCDM’s performance was dependent on how the yes;nof g decisions in the test data set were manually labeled;(2)separating the fuzzy inference rules into a yesf g group and a nof g group and compute the results sep-arately yielded potentially better decision accuracy.Furthermore,The gene expression pro-gramming model(GEPM)performed better than the improved FIS-based model.The findings led the authors to suggest two possible research directions:(1)add the subject vehicle’s speed as an input to the LCDM and redesign the decision-making model;(2)con-struct models for congested and uncongested traffic separately.The authors further sug-gested the use of instrumented vehicles to collect a set of high-fidelity lane changing data in the naturalistic driving environment.展开更多
文摘The lane changing decision model(LCDM)is a critical component in semi-and fully-automated driving systems.Recent research has found that the fuzzy inference system(FIS)is a promising approach to implementing LCDMs.To improve the FIS’s performance,this research reviewed the challenges in the development an FIS model to make the yes;nof g decisions in discretionary lane changes.The FIS model was revised to bring its fuzzy inference rules more consistent with the fuzzy membership functions,and its com-position and defuzzification methods more in line with the classical fuzzy logic theory.An equitable test data set with approximately equal number of yes;nof g data points was assembled from the same next generation simulation(NGSIM)data used in the past research.The test results proved that:(1)an LCDM’s performance was dependent on how the yes;nof g decisions in the test data set were manually labeled;(2)separating the fuzzy inference rules into a yesf g group and a nof g group and compute the results sep-arately yielded potentially better decision accuracy.Furthermore,The gene expression pro-gramming model(GEPM)performed better than the improved FIS-based model.The findings led the authors to suggest two possible research directions:(1)add the subject vehicle’s speed as an input to the LCDM and redesign the decision-making model;(2)con-struct models for congested and uncongested traffic separately.The authors further sug-gested the use of instrumented vehicles to collect a set of high-fidelity lane changing data in the naturalistic driving environment.
