Based on the driver surveillance video data and controller area network(CAN)data,the methods of studying commercial vehicles’driving behavior is relatively advanced.However,these methods have difficulty in covering p...Based on the driver surveillance video data and controller area network(CAN)data,the methods of studying commercial vehicles’driving behavior is relatively advanced.However,these methods have difficulty in covering private vehicles.Naturalistic driving studies have disadvantages of small sample size and high cost,one new driving behavior evaluation method using massive vehicle trajectory data is put forward.An automatic encoding machine is used to reduce the noise of raw data,and then driving dynamics and self-organizing mapping(SOM)classification are used to give thresholds or the judgement method of overspeed,rapid speed change,rapid turning and rapid lane changing.The proportion of different driving behaviors and typical dangerous driving behaviors is calculated,then the temporal and spatial distribution of drivers’driving behavior and the driving behavior characteristics on typical roads are analyzed.Driving behaviors on accident-prone road sections and normal road sections are compared.Results show that in Shenzhen,frequent lane changing and overspeed are the most common unsafe driving behaviors;16.1%drivers have relatively aggressive driving behavior;the proportion of dangerous driving behavior is higher outside the original economic special zone;dangerous driving behavior is highly correlated with traffic accident frequency.展开更多
The three dimensional variable cross-section roll forming is a kind of new metal forming technol- ogy which combines large forming force, multi-axis linkage movement and space synergic movement, and the sequential syn...The three dimensional variable cross-section roll forming is a kind of new metal forming technol- ogy which combines large forming force, multi-axis linkage movement and space synergic movement, and the sequential synergic movement of the ganged roller group is used to complete the metal sheet forming according to the shape of the complicated and variable forming part data. The control system should meet the demands of quick response to the test requirements of the product part. A new kind of real time data driving multi-axis linkage and synergic movement control strategy of 3D roll forming is put forward in the paper. In the new control strategy, the forming data are automatically generated according to the shape of the parts, and the multi-axis linkage movement together with cooperative motion among the six stands of the 3D roll forming machine is driven by the real-time information, and the control nodes are also driven by the forming data. The new control strategy is applied to a 48 axis 3D roll forming machine developed by our research center, and the control servo period is less than 10ms. A forming experiment of variable cross section part is carried out, and the forming preci- sion is better than ＋ 0.5mm by the control strategy. The result of the experiment proves that the control strategy has significant potentiality for the development of 3D roll forming production line with large scale, multi-axis ganged and svner^ic movement展开更多
Rural intersections account for around 30% of crashes in rural areas and 6% of all fatal crashes, representing a significant but poorly understood safety problem. Crashes at rural intersections are also problematic si...Rural intersections account for around 30% of crashes in rural areas and 6% of all fatal crashes, representing a significant but poorly understood safety problem. Crashes at rural intersections are also problematic since high speeds on intersection approaches are present which can exacerbate the impact of a crash. Additionally, rural areas are often underserved with EMS services which can further contribute to negative crash outcomes. This paper describes an analysis of driver stopping behavior at rural T-intersections using the SHRP 2 Naturalistic Driving Study data. Type of stop was used as a safety surrogate measure using full/rolling stops compared to non-stops. Time series traces were obtained for 157 drivers at 87 unique intersections resulting in 1277 samples at the stop controlled approach for T-intersections. Roadway (i.e. number of lanes, presence of skew, speed limit, presence of stop bar or other traffic control devices), driver (age, gender, speeding), and environmental characteristics (time of day, presence of rain) were reduced and included as independent variables. Results of a logistic regression model indicated drivers were less likely to stop during the nighttime. However presence of intersection lighting increased the likelihood of full/rolling stops. Presence of intersection skew was shown to negatively impact stopping behavior. Additionally drivers who were traveling over the posted speed limit upstream of the intersection approach were less likely to stop at the approach stop sign.展开更多
Identifying the driving forces that cause changes in forest ecosystem services related to water conservation is essential for the design of interventions that could enhance positive impacts as well as minimizing negat...Identifying the driving forces that cause changes in forest ecosystem services related to water conservation is essential for the design of interventions that could enhance positive impacts as well as minimizing negative impacts. In this study, we propose an assessment concept framework model for indirect-direct-ecosystem service （IN-DI-ESS） driving forces within this context and method for index construction that considers the selection of a robust and parsimonious variable set. Factor analysis was integrated into two-stage data envelopment analysis （TS-DEA） to determine the driving forces and their effects on water conservation services in forest ecosystems at the provincial scale in China. The results showed the following. 1） Ten indicators with factor scores more than 0.8 were selected as the minimum data set. Four indicators comprising population density, per capita gross domestic product, irrigation efficiency, and per capita food consumption were the indirect driving factors, and six indicators comprising precipitation, farmland into forestry or pasture, forest cover, habitat area, water footprint, and wood extraction were the direct driving forces. 2） Spearman＇s rank correlation test was performed to compare the overall effectiveness in two periods： stage 1 and stage 2. The calculated coefficients were 0.245, 0.136, and 0.579, respectively, whereas the tabulated value was 0.562. This indicates that the driving forces obviously differed in terms of their contribution to the overall effectiveness and they caused changes in water conservation services in different stages. In terms of the variations in different driving force effects in the years 2000 and 2010, the overall, stage 1, and stage 2 variances were 0.020, 0.065, and 0.079 in 2000, respectively, and 0.018, 0.063, and 0.071 in 2010. This also indicates that heterogeneous driving force effects were obvious in the process during the same period. Identifying the driving forces that affect service changes and evaluating their efficiency have significant policy implications for the management of forest ecosystem services. Advanced effectiveness measures for weak regions could be improved in an appropriate manner. In this study, we showed that factor analysis coupled with TS-DEA based on the IN-D1-ESS framework can increase the parsimony of driving force indicators, as well as interpreting the interactions among indirect and direct driving forces with forest ecosystem water conservation services, and reducing the uncertainty related to the internal consistency during data selection.展开更多
Takeover safety draws increasing attention in the intelligent transportation as the new energy vehicles with cutting-edge autopilot capabilities vigorously blossom on the road.Despite recent studies highlighting the i...Takeover safety draws increasing attention in the intelligent transportation as the new energy vehicles with cutting-edge autopilot capabilities vigorously blossom on the road.Despite recent studies highlighting the importance of drivers’emotions in takeover safety,the lack of emotion-aware takeover datasets hinders further investigation,thereby constraining potential applications in this field.To this end,we introduce ViE-Take,the first Vision-driven(Vision is used since it constitutes the most cost-effective and user-friendly solution for commercial driver monitor systems)dataset for exploring the Emotional landscape in Takeovers of autonomous driving.ViE-Take enables a comprehensive exploration of the impact of emotions on drivers’takeover performance through 3 key attributes:multi-source emotion elicitation,multi-modal driver data collection,and multi-dimensional emotion annotations.To aid the use of ViE-Take,we provide 4 deep models(corresponding to 4 prevalent learning strategies)for predicting 3 different aspects of drivers’takeover performance(readiness,reaction time,and quality).These models offer benefits for various downstream tasks,such as driver emotion recognition and regulation for automobile manufacturers.Initial analysis and experiments conducted on ViE-Take indicate that(a)emotions have diverse impacts on takeover performance,some of which are counterintuitive;(b)highly expressive social media clips,despite their brevity,prove effective in eliciting emotions(a foundation for emotion regulation);and(c)predicting takeover performance solely through deep learning on vision data not only is feasible but also holds great potential.展开更多
基金The National Natural Science Foundation of China(No.71641005)the National Key Research and Development Program of China(No.2018YFB1601105)
文摘Based on the driver surveillance video data and controller area network(CAN)data,the methods of studying commercial vehicles’driving behavior is relatively advanced.However,these methods have difficulty in covering private vehicles.Naturalistic driving studies have disadvantages of small sample size and high cost,one new driving behavior evaluation method using massive vehicle trajectory data is put forward.An automatic encoding machine is used to reduce the noise of raw data,and then driving dynamics and self-organizing mapping(SOM)classification are used to give thresholds or the judgement method of overspeed,rapid speed change,rapid turning and rapid lane changing.The proportion of different driving behaviors and typical dangerous driving behaviors is calculated,then the temporal and spatial distribution of drivers’driving behavior and the driving behavior characteristics on typical roads are analyzed.Driving behaviors on accident-prone road sections and normal road sections are compared.Results show that in Shenzhen,frequent lane changing and overspeed are the most common unsafe driving behaviors;16.1%drivers have relatively aggressive driving behavior;the proportion of dangerous driving behavior is higher outside the original economic special zone;dangerous driving behavior is highly correlated with traffic accident frequency.
基金Supported by National Key Technology R&D Program(No.2011BAG03B03)
文摘The three dimensional variable cross-section roll forming is a kind of new metal forming technol- ogy which combines large forming force, multi-axis linkage movement and space synergic movement, and the sequential synergic movement of the ganged roller group is used to complete the metal sheet forming according to the shape of the complicated and variable forming part data. The control system should meet the demands of quick response to the test requirements of the product part. A new kind of real time data driving multi-axis linkage and synergic movement control strategy of 3D roll forming is put forward in the paper. In the new control strategy, the forming data are automatically generated according to the shape of the parts, and the multi-axis linkage movement together with cooperative motion among the six stands of the 3D roll forming machine is driven by the real-time information, and the control nodes are also driven by the forming data. The new control strategy is applied to a 48 axis 3D roll forming machine developed by our research center, and the control servo period is less than 10ms. A forming experiment of variable cross section part is carried out, and the forming preci- sion is better than ＋ 0.5mm by the control strategy. The result of the experiment proves that the control strategy has significant potentiality for the development of 3D roll forming production line with large scale, multi-axis ganged and svner^ic movement
文摘Rural intersections account for around 30% of crashes in rural areas and 6% of all fatal crashes, representing a significant but poorly understood safety problem. Crashes at rural intersections are also problematic since high speeds on intersection approaches are present which can exacerbate the impact of a crash. Additionally, rural areas are often underserved with EMS services which can further contribute to negative crash outcomes. This paper describes an analysis of driver stopping behavior at rural T-intersections using the SHRP 2 Naturalistic Driving Study data. Type of stop was used as a safety surrogate measure using full/rolling stops compared to non-stops. Time series traces were obtained for 157 drivers at 87 unique intersections resulting in 1277 samples at the stop controlled approach for T-intersections. Roadway (i.e. number of lanes, presence of skew, speed limit, presence of stop bar or other traffic control devices), driver (age, gender, speeding), and environmental characteristics (time of day, presence of rain) were reduced and included as independent variables. Results of a logistic regression model indicated drivers were less likely to stop during the nighttime. However presence of intersection lighting increased the likelihood of full/rolling stops. Presence of intersection skew was shown to negatively impact stopping behavior. Additionally drivers who were traveling over the posted speed limit upstream of the intersection approach were less likely to stop at the approach stop sign.
基金Under the auspices of Science and Technology Service Network Initiative Project of the Chinese Academy of Sciences(No.KFJ-EW-STS-002)
文摘Identifying the driving forces that cause changes in forest ecosystem services related to water conservation is essential for the design of interventions that could enhance positive impacts as well as minimizing negative impacts. In this study, we propose an assessment concept framework model for indirect-direct-ecosystem service （IN-DI-ESS） driving forces within this context and method for index construction that considers the selection of a robust and parsimonious variable set. Factor analysis was integrated into two-stage data envelopment analysis （TS-DEA） to determine the driving forces and their effects on water conservation services in forest ecosystems at the provincial scale in China. The results showed the following. 1） Ten indicators with factor scores more than 0.8 were selected as the minimum data set. Four indicators comprising population density, per capita gross domestic product, irrigation efficiency, and per capita food consumption were the indirect driving factors, and six indicators comprising precipitation, farmland into forestry or pasture, forest cover, habitat area, water footprint, and wood extraction were the direct driving forces. 2） Spearman＇s rank correlation test was performed to compare the overall effectiveness in two periods： stage 1 and stage 2. The calculated coefficients were 0.245, 0.136, and 0.579, respectively, whereas the tabulated value was 0.562. This indicates that the driving forces obviously differed in terms of their contribution to the overall effectiveness and they caused changes in water conservation services in different stages. In terms of the variations in different driving force effects in the years 2000 and 2010, the overall, stage 1, and stage 2 variances were 0.020, 0.065, and 0.079 in 2000, respectively, and 0.018, 0.063, and 0.071 in 2010. This also indicates that heterogeneous driving force effects were obvious in the process during the same period. Identifying the driving forces that affect service changes and evaluating their efficiency have significant policy implications for the management of forest ecosystem services. Advanced effectiveness measures for weak regions could be improved in an appropriate manner. In this study, we showed that factor analysis coupled with TS-DEA based on the IN-D1-ESS framework can increase the parsimony of driving force indicators, as well as interpreting the interactions among indirect and direct driving forces with forest ecosystem water conservation services, and reducing the uncertainty related to the internal consistency during data selection.
基金supported by the National Natural Science Foundation of China(no.62072153)the Anhui Provincial Key Technologies R&D Program(no.2022h11020015)the 111 Center(no.B14025).
文摘Takeover safety draws increasing attention in the intelligent transportation as the new energy vehicles with cutting-edge autopilot capabilities vigorously blossom on the road.Despite recent studies highlighting the importance of drivers’emotions in takeover safety,the lack of emotion-aware takeover datasets hinders further investigation,thereby constraining potential applications in this field.To this end,we introduce ViE-Take,the first Vision-driven(Vision is used since it constitutes the most cost-effective and user-friendly solution for commercial driver monitor systems)dataset for exploring the Emotional landscape in Takeovers of autonomous driving.ViE-Take enables a comprehensive exploration of the impact of emotions on drivers’takeover performance through 3 key attributes:multi-source emotion elicitation,multi-modal driver data collection,and multi-dimensional emotion annotations.To aid the use of ViE-Take,we provide 4 deep models(corresponding to 4 prevalent learning strategies)for predicting 3 different aspects of drivers’takeover performance(readiness,reaction time,and quality).These models offer benefits for various downstream tasks,such as driver emotion recognition and regulation for automobile manufacturers.Initial analysis and experiments conducted on ViE-Take indicate that(a)emotions have diverse impacts on takeover performance,some of which are counterintuitive;(b)highly expressive social media clips,despite their brevity,prove effective in eliciting emotions(a foundation for emotion regulation);and(c)predicting takeover performance solely through deep learning on vision data not only is feasible but also holds great potential.
