EXCERPTS FROM MAJOR CHINESE MAGAZINES SMART DRIVING SAFETY DEBATE China Newsweek 14 April A fatal Xiaomi SU7 crash on 29 March involving NOA(navigation on autopilot)-assisted driving mode has ignited debates about sma...EXCERPTS FROM MAJOR CHINESE MAGAZINES SMART DRIVING SAFETY DEBATE China Newsweek 14 April A fatal Xiaomi SU7 crash on 29 March involving NOA(navigation on autopilot)-assisted driving mode has ignited debates about smart driving safety in China.Preliminary data shows the car was in NOA mode before colliding with highway barriers,killing three people.展开更多
The impact process between car driver＇s head and seat head restraint during a car rear end collision is simulated, where the multiphase and nonlinear features of the human brain tissues and the foam material of the s...The impact process between car driver＇s head and seat head restraint during a car rear end collision is simulated, where the multiphase and nonlinear features of the human brain tissues and the foam material of the seat head restraint are considered. Using the FEM-software I,S-DYNA, the brain tissue deformation caused by a ear REC is calculated. The purpose of this work is to supply references for improving the design of the head protective devices. The results show that the maximum shear strain appears near the boundaries of different phases and there is a great shear strain gradient in the brain tissues.展开更多
Objective To estimate the association of driver sleepiness with the risk of car crashes. Methods A population-based case-control study was conducted in Shenyang, a northeastern city in China, between November 2001 and...Objective To estimate the association of driver sleepiness with the risk of car crashes. Methods A population-based case-control study was conducted in Shenyang, a northeastern city in China, between November 2001 and July 2002. The case group comprised 406 car drivers involved in crashes, and 438 car drivers recruited at randomly selected sites, and on the day of week, and the time of day when they were driving on highways in the study region during the study period were used as control groups. Face-to-face interviews with drivers were conducted according to a well-structured questionnaire covering the circumstances of their current trip and their background information. Stanford sleepiness scale and Epworth sleepiness scale were used to quantify acute sleepiness and chronic sleepiness respectively. Results There was a strong association between chronic sleepiness and the risk of car crash. Significantly increased risk of crash was associated with drivers who identified themselves as sleepy (Epworth sleepiness score≥10 vs <10; adjusted odds ratio 2.07, 95% confidence interval 1.30 to 3.29), but no increased risk was associated with measures of acute sleepiness. Conclusions Chronic sleepiness in car drivers significantly increases the risk of car crash. Reductions in road traffic injuries may be achieved if fewer people drive when they are sleepy.展开更多
In the continually evolving landscape of data-driven methodologies addressing car crash patterns,a holistic analysis remains critical to decode the complex nuances of this phenomenon.This study bridges this knowledge ...In the continually evolving landscape of data-driven methodologies addressing car crash patterns,a holistic analysis remains critical to decode the complex nuances of this phenomenon.This study bridges this knowledge gap with a robust examination of car crash occurrence dynamics and the influencing variables in the Greater Melbourne area,Australia.We employed a comprehensive multi-model machine learning and geospatial analytics approach,unveiling the complicated interactions intrinsic to vehicular incidents.By harnessing Random Forest with SHAP(Shapley Additive Explanations),GLR(Generalized Linear Regression),and GWR(Geographically Weighted Regression),our research not only highlighted pivotal contributing elements but also enriched our findings by capturing often overlooked complexities.Using the Random Forest model,essential factors were emphasized,and with the aid of SHAP,we accessed the interaction of these factors.To complement our methodology,we incorporated hexagonalized geographic units,refining the granularity of crash density evaluations.In our multi-model study of car crash dynamics in Greater Melbourne,road geometry emerged as a key factor,with intersections showing a significant positive correlation with crashes.The average land surface temperature had variable significance across scales.Socio-economically,regions with a higher proportion of childless populations were identified as more prone to accidents.Public transit usage displayed a strong positive association with crashes,especially in densely populated areas.The convergence of insights from both Generalized Linear Regression and Random Forest’s SHAP values offered a comprehensive understanding of underlying patterns,pinpointing high-risk zones and influential determinants.These findings offer pivotal insights for targeted safety interventions in Greater Melbourne,Australia.展开更多
文摘EXCERPTS FROM MAJOR CHINESE MAGAZINES SMART DRIVING SAFETY DEBATE China Newsweek 14 April A fatal Xiaomi SU7 crash on 29 March involving NOA(navigation on autopilot)-assisted driving mode has ignited debates about smart driving safety in China.Preliminary data shows the car was in NOA mode before colliding with highway barriers,killing three people.
基金Sponsored by the Basic Research Foundation of Beijing Institute of Technology (05120246E)
文摘The impact process between car driver＇s head and seat head restraint during a car rear end collision is simulated, where the multiphase and nonlinear features of the human brain tissues and the foam material of the seat head restraint are considered. Using the FEM-software I,S-DYNA, the brain tissue deformation caused by a ear REC is calculated. The purpose of this work is to supply references for improving the design of the head protective devices. The results show that the maximum shear strain appears near the boundaries of different phases and there is a great shear strain gradient in the brain tissues.
文摘Objective To estimate the association of driver sleepiness with the risk of car crashes. Methods A population-based case-control study was conducted in Shenyang, a northeastern city in China, between November 2001 and July 2002. The case group comprised 406 car drivers involved in crashes, and 438 car drivers recruited at randomly selected sites, and on the day of week, and the time of day when they were driving on highways in the study region during the study period were used as control groups. Face-to-face interviews with drivers were conducted according to a well-structured questionnaire covering the circumstances of their current trip and their background information. Stanford sleepiness scale and Epworth sleepiness scale were used to quantify acute sleepiness and chronic sleepiness respectively. Results There was a strong association between chronic sleepiness and the risk of car crash. Significantly increased risk of crash was associated with drivers who identified themselves as sleepy (Epworth sleepiness score≥10 vs <10; adjusted odds ratio 2.07, 95% confidence interval 1.30 to 3.29), but no increased risk was associated with measures of acute sleepiness. Conclusions Chronic sleepiness in car drivers significantly increases the risk of car crash. Reductions in road traffic injuries may be achieved if fewer people drive when they are sleepy.
基金Linking Health,Place and Urban Planning through the Australian Urban Observatory by Ian Potter Foundation,Australia.
文摘In the continually evolving landscape of data-driven methodologies addressing car crash patterns,a holistic analysis remains critical to decode the complex nuances of this phenomenon.This study bridges this knowledge gap with a robust examination of car crash occurrence dynamics and the influencing variables in the Greater Melbourne area,Australia.We employed a comprehensive multi-model machine learning and geospatial analytics approach,unveiling the complicated interactions intrinsic to vehicular incidents.By harnessing Random Forest with SHAP(Shapley Additive Explanations),GLR(Generalized Linear Regression),and GWR(Geographically Weighted Regression),our research not only highlighted pivotal contributing elements but also enriched our findings by capturing often overlooked complexities.Using the Random Forest model,essential factors were emphasized,and with the aid of SHAP,we accessed the interaction of these factors.To complement our methodology,we incorporated hexagonalized geographic units,refining the granularity of crash density evaluations.In our multi-model study of car crash dynamics in Greater Melbourne,road geometry emerged as a key factor,with intersections showing a significant positive correlation with crashes.The average land surface temperature had variable significance across scales.Socio-economically,regions with a higher proportion of childless populations were identified as more prone to accidents.Public transit usage displayed a strong positive association with crashes,especially in densely populated areas.The convergence of insights from both Generalized Linear Regression and Random Forest’s SHAP values offered a comprehensive understanding of underlying patterns,pinpointing high-risk zones and influential determinants.These findings offer pivotal insights for targeted safety interventions in Greater Melbourne,Australia.
