Purpose–Heavy fog results in low visibility,which increases the probability and severity of traffic crashes,and fog warning system is conducive to the reduction of crashes by conveying warning messages to drivers.This...Purpose–Heavy fog results in low visibility,which increases the probability and severity of traffic crashes,and fog warning system is conducive to the reduction of crashes by conveying warning messages to drivers.This paper aims at exploring the effects of dynamic message sign(DMS)of fog warning system on driver performance.Design/methodology/approach–First,a testing platform was established based on driving simulator and driver performance data under DMS were collected.The experiment route was consisted of three different zones(i.e.warning zone,transition zone and heavy fog zone),and mean speed,mean acceleration,mean jerk in the whole zone,ending speed in the warning zone and transition zone,maximum deceleration rate and mean speed reduction proportion in the transition zone and heavy fog zone were selected.Next,the one-way analysis of variance was applied to test the significant difference between the metrics.Besides,drivers’subjective perception was also considered.Findings–The results indicated that DMS is beneficial to reduce speed before drivers enter the heavy fog zone.Besides,when drivers enter a heavy fog zone,DMS can reduce the tension of drivers and make drivers operate more smoothly.Originality/value–This paper provides a comprehensive approach for evaluating the effectiveness of the warning system in adverse conditions based on the driving simulation test platform.The method can be extended to the evaluation of vehicle-to-infrastructure technology in other special scenarios.展开更多
A novel algorithm for vehicle average velocity detection through automatic and dynamic camera calibration based on dark channel in homogenous fog weather condition is presented in this paper. Camera fixed in the middl...A novel algorithm for vehicle average velocity detection through automatic and dynamic camera calibration based on dark channel in homogenous fog weather condition is presented in this paper. Camera fixed in the middle of the road should be calibrated in homogenous fog weather condition, and can be used in any weather condition. Unlike other researches in velocity calculation area, our traffic model only includes road plane and vehicles in motion. Painted lines in scene image are neglected because sometimes there are no traffic lanes, especially in un-structured traffic scene. Once calibrated, scene distance will be got and can be used to calculate vehicles average velocity. Three major steps are included in our algorithm. Firstly, current video frame is recognized to discriminate current weather condition based on area search method （ASM）. If it is homogenous fog, average pixel value from top to bottom in the selected area will change in the form of edge spread function （ESF）. Secondly, traffic road surface plane will be found by generating activity map created by calculating the expected value of the absolute intensity difference between two adjacent frames. Finally, scene transmission image is got by dark channel prior theory, camera s intrinsic and extrinsic parameters are calculated based on the parameter calibration formula deduced from monocular model and scene transmission image. In this step, several key points with particular transmission value for generating necessary calculation equations on road surface are selected to calibrate the camera. Vehicles pixel coordinates are transformed to camera coordinates. Distance between vehicles and the camera will be calculated, and then average velocity for each vehicle is got. At the end of this paper, calibration results and vehicles velocity data for nine vehicles in different weather conditions are given. Comparison with other algorithms verifies the effectiveness of our algorithm.展开更多
文摘Purpose–Heavy fog results in low visibility,which increases the probability and severity of traffic crashes,and fog warning system is conducive to the reduction of crashes by conveying warning messages to drivers.This paper aims at exploring the effects of dynamic message sign(DMS)of fog warning system on driver performance.Design/methodology/approach–First,a testing platform was established based on driving simulator and driver performance data under DMS were collected.The experiment route was consisted of three different zones(i.e.warning zone,transition zone and heavy fog zone),and mean speed,mean acceleration,mean jerk in the whole zone,ending speed in the warning zone and transition zone,maximum deceleration rate and mean speed reduction proportion in the transition zone and heavy fog zone were selected.Next,the one-way analysis of variance was applied to test the significant difference between the metrics.Besides,drivers’subjective perception was also considered.Findings–The results indicated that DMS is beneficial to reduce speed before drivers enter the heavy fog zone.Besides,when drivers enter a heavy fog zone,DMS can reduce the tension of drivers and make drivers operate more smoothly.Originality/value–This paper provides a comprehensive approach for evaluating the effectiveness of the warning system in adverse conditions based on the driving simulation test platform.The method can be extended to the evaluation of vehicle-to-infrastructure technology in other special scenarios.
基金supported by National High Technology Research and Development Program of China(863 Program)(No. 2011AA110301)National Natural Science Foundation of China(No. 61079001)the Ph. D. Programs Foundation of Ministry of Education of China(No. 20111103110017)
文摘A novel algorithm for vehicle average velocity detection through automatic and dynamic camera calibration based on dark channel in homogenous fog weather condition is presented in this paper. Camera fixed in the middle of the road should be calibrated in homogenous fog weather condition, and can be used in any weather condition. Unlike other researches in velocity calculation area, our traffic model only includes road plane and vehicles in motion. Painted lines in scene image are neglected because sometimes there are no traffic lanes, especially in un-structured traffic scene. Once calibrated, scene distance will be got and can be used to calculate vehicles average velocity. Three major steps are included in our algorithm. Firstly, current video frame is recognized to discriminate current weather condition based on area search method （ASM）. If it is homogenous fog, average pixel value from top to bottom in the selected area will change in the form of edge spread function （ESF）. Secondly, traffic road surface plane will be found by generating activity map created by calculating the expected value of the absolute intensity difference between two adjacent frames. Finally, scene transmission image is got by dark channel prior theory, camera s intrinsic and extrinsic parameters are calculated based on the parameter calibration formula deduced from monocular model and scene transmission image. In this step, several key points with particular transmission value for generating necessary calculation equations on road surface are selected to calibrate the camera. Vehicles pixel coordinates are transformed to camera coordinates. Distance between vehicles and the camera will be calculated, and then average velocity for each vehicle is got. At the end of this paper, calibration results and vehicles velocity data for nine vehicles in different weather conditions are given. Comparison with other algorithms verifies the effectiveness of our algorithm.
