Analyzing the driving behavior of autonomous vehicles(AV)in mixed traffic conditions at urban intersections has become increasingly important for improving intersection design,providing infrastructure-based guidance i...Analyzing the driving behavior of autonomous vehicles(AV)in mixed traffic conditions at urban intersections has become increasingly important for improving intersection design,providing infrastructure-based guidance information,and developing capability-enhanced AV perception systems.This study investigated the contributing factors affecting AV driving behavior using theWaymo Open Dataset.Binarized autonomous driving stability metrics,derived via a kernel density estimation,served as the target variables for a random forest classification model.The model’s input variables included 15 factors divided into four types:intersection-related,surrounding object-related,road infrastructure-related,and time-of-day-related types.The random forest classification model was employed to identify the key factors affecting autonomous driving behavior.In addition,the identified factors were further ranked based on feature importance.SHAP analysis was utilized to enhance model interpretability by quantifying the contribution of each factor and identifying their directional impacts.The type of intersection factor was found to have an importance of 0.243 and was the most influential factor on autonomous driving behavior.On average,intersection-related factors had an importance of 0.196,which is approximately a 31.1%margin over the average importance of surrounding object-related factors.Additionally,the surrounding object-related factors that were collected through sensors on the autonomous vehicle had a high degree of feature importance,especially with the number of pedestrians having the highest importance(0.107)of the types of objects.The correlation between these findings can contribute to the development of various treatments to improvemore harmonized AVs’maneuvering with other road users and facilities in urban mixed traffic environments.展开更多
Considering the situation that it is difficult to control the stability of narrow coal pillar in gob-side entry driving under unstable overlying strata, the finite difference numerical simulation method was adopted to...Considering the situation that it is difficult to control the stability of narrow coal pillar in gob-side entry driving under unstable overlying strata, the finite difference numerical simulation method was adopted to analyze the inner stress distribution and its evolution regularity, as well as the deformation characteristics of narrow coal pillar in gob-side entry driving, in the whole process from entry driving of last working face to the present working face mining. A new method of narrow coal pillar control based on the triune coupling support technique (TCST), which includes that high-strength prestressed thread steel bolt is used to strain the coal on the goaf side, and that short bolt to control the integrity of global displacement zone in coal pillar on the entry side, and that long grouting cable to fix anchor point to constrain the bed separation between global displacement zone and fixed zone, is thereby generated and applied to the field production. The result indicates that after entry excavating along the gob under unstable overlying strata, the supporting structure left on the gob side of narrow coal pillar is basically invalid to maintain the coal-pillar stability, and the large deformation of the pillar on the gob side is evident. Except for the significant dynamic pressure appearing in the coal mining of last working face and overlying strata stabilizing process, the stress variation inside the coal pillar in other stages are rather steady, however, the stress expansion is obvious and the coal pillar continues to deform. Once the gob-side entry driving is completed, a global displacement zone on the entry side appears in the shallow part of the pillar, whereas, a relatively steady fixed zone staying almost still in gob-side entry driving and present working face mining is found in the deep part of the pillar. The application of TCST can not only avoid the failure of pillar supporting structure, but exert the supporting capacity of the bolting structure left in the pillar of last sublevel entry, thus to jointly maintain the stability of coal pillar.展开更多
In the light of the visual angle model(VAM),an improved car-following model considering driver's visual angle,anticipated time and stabilizing driving behavior is proposed so as to investigate how the driver's...In the light of the visual angle model(VAM),an improved car-following model considering driver's visual angle,anticipated time and stabilizing driving behavior is proposed so as to investigate how the driver's behavior factors affect the stability of the traffic flow.Based on the model,linear stability analysis is performed together with bifurcation analysis,whose corresponding stability condition is highly fit to the results of the linear analysis.Furthermore,the time-dependent Ginzburg–Landau(TDGL)equation and the modified Korteweg–de Vries(m Kd V)equation are derived by nonlinear analysis,and we obtain the relationship of the two equations through the comparison.Finally,parameter calibration and numerical simulation are conducted to verify the validity of the theoretical analysis,whose results are highly consistent with the theoretical analysis.展开更多
基金supported by Korea Institute of Police Technology(KIPoT)grant funded by the Korea government(KNPA)(Project Name:Development of Lv.4 Driving Ability Evaluation Technology for Autonomous Vehicles Based on Real Roads/Project Number:RS-2023-00238253).
文摘Analyzing the driving behavior of autonomous vehicles(AV)in mixed traffic conditions at urban intersections has become increasingly important for improving intersection design,providing infrastructure-based guidance information,and developing capability-enhanced AV perception systems.This study investigated the contributing factors affecting AV driving behavior using theWaymo Open Dataset.Binarized autonomous driving stability metrics,derived via a kernel density estimation,served as the target variables for a random forest classification model.The model’s input variables included 15 factors divided into four types:intersection-related,surrounding object-related,road infrastructure-related,and time-of-day-related types.The random forest classification model was employed to identify the key factors affecting autonomous driving behavior.In addition,the identified factors were further ranked based on feature importance.SHAP analysis was utilized to enhance model interpretability by quantifying the contribution of each factor and identifying their directional impacts.The type of intersection factor was found to have an importance of 0.243 and was the most influential factor on autonomous driving behavior.On average,intersection-related factors had an importance of 0.196,which is approximately a 31.1%margin over the average importance of surrounding object-related factors.Additionally,the surrounding object-related factors that were collected through sensors on the autonomous vehicle had a high degree of feature importance,especially with the number of pedestrians having the highest importance(0.107)of the types of objects.The correlation between these findings can contribute to the development of various treatments to improvemore harmonized AVs’maneuvering with other road users and facilities in urban mixed traffic environments.
基金supports from the National High Technology Research and Development Program of China (No. 2012AA062101)the Program for New Century Excellent Talents in University of Ministry of Education of China (No. NCET-10-0770)+1 种基金the Program Granted for Scientific Innovation Research of College Graduate in Jiangsu Province (No. CXZZ11-0309)the Priority Academic Program Development of Jiangsu Higher Education Institutions (No. SZBF2011-6-B35)
文摘Considering the situation that it is difficult to control the stability of narrow coal pillar in gob-side entry driving under unstable overlying strata, the finite difference numerical simulation method was adopted to analyze the inner stress distribution and its evolution regularity, as well as the deformation characteristics of narrow coal pillar in gob-side entry driving, in the whole process from entry driving of last working face to the present working face mining. A new method of narrow coal pillar control based on the triune coupling support technique (TCST), which includes that high-strength prestressed thread steel bolt is used to strain the coal on the goaf side, and that short bolt to control the integrity of global displacement zone in coal pillar on the entry side, and that long grouting cable to fix anchor point to constrain the bed separation between global displacement zone and fixed zone, is thereby generated and applied to the field production. The result indicates that after entry excavating along the gob under unstable overlying strata, the supporting structure left on the gob side of narrow coal pillar is basically invalid to maintain the coal-pillar stability, and the large deformation of the pillar on the gob side is evident. Except for the significant dynamic pressure appearing in the coal mining of last working face and overlying strata stabilizing process, the stress variation inside the coal pillar in other stages are rather steady, however, the stress expansion is obvious and the coal pillar continues to deform. Once the gob-side entry driving is completed, a global displacement zone on the entry side appears in the shallow part of the pillar, whereas, a relatively steady fixed zone staying almost still in gob-side entry driving and present working face mining is found in the deep part of the pillar. The application of TCST can not only avoid the failure of pillar supporting structure, but exert the supporting capacity of the bolting structure left in the pillar of last sublevel entry, thus to jointly maintain the stability of coal pillar.
基金the Natural Science Foundation of Zhejiang Province,China(Grant Nos.LY22G010001,LY20G010004)the Program of Humanities and Social Science of Education Ministry of China(Grant No.20YJA630008)+1 种基金the National Key Research and Development Program of China-Traffic Modeling,Surveillance and Control with Connected&Automated Vehicles(Grant No.2017YFE9134700)the K.C.Wong Magna Fund in Ningbo University,China。
文摘In the light of the visual angle model(VAM),an improved car-following model considering driver's visual angle,anticipated time and stabilizing driving behavior is proposed so as to investigate how the driver's behavior factors affect the stability of the traffic flow.Based on the model,linear stability analysis is performed together with bifurcation analysis,whose corresponding stability condition is highly fit to the results of the linear analysis.Furthermore,the time-dependent Ginzburg–Landau(TDGL)equation and the modified Korteweg–de Vries(m Kd V)equation are derived by nonlinear analysis,and we obtain the relationship of the two equations through the comparison.Finally,parameter calibration and numerical simulation are conducted to verify the validity of the theoretical analysis,whose results are highly consistent with the theoretical analysis.
