An important and challenging aspect of developing an intelligent transportation system is the identification of nighttime vehicles. Most accidents occur at night owing to the absence of night lighting conditions. Vehi...An important and challenging aspect of developing an intelligent transportation system is the identification of nighttime vehicles. Most accidents occur at night owing to the absence of night lighting conditions. Vehicle detection has become a vital subject for research to ensure safety and avoid accidents. New vision-based on-road nighttime vehicle detection and tracking system are suggested in this survey paper using taillight and headlight features. Using computer vision and some image processing techniques, the proposed system can identify vehicles based on taillight and headlight features. For vehicle tracking, a centroid tracking algorithm has been used. Euclidean Distance method has been used for measuring the distances between two neighboring objects and tracks the nearest neighbor. In the proposed system two flexible fixed Region of Interest (ROI) have been used, one is the Headlight ROI, and another is the Taillight ROI that could adapt to different resolutions of the images and videos. The achievement of this research work is that the proposed two ROIs can work simultaneously in a frame to identify oncoming and preceding vehicles at night. The segmentation techniques and double thresholding method have been used to extract the red and white components from the scene to identify the vehicle headlights and taillights. To evaluate the capability of the proposed process, two types of datasets have been used. Experimental findings indicate that the performance of the proposed technique is reliable and effective in distinct nighttime environments for detection and tracking of vehicles. The proposed method has been able to detect and track double lights as well as single light such as motorcycle light and achieved average accuracy and average processing time of vehicle detection about 97.22% and 0.01 s per frame respectively.展开更多
The controller in an automated vehicle relies on sensors to collect the information needed for handling traffic situations,and reducing the frequency of using sensors could prolong theirlifespans.We present in this pa...The controller in an automated vehicle relies on sensors to collect the information needed for handling traffic situations,and reducing the frequency of using sensors could prolong theirlifespans.We present in this paper the application of dynamic sensor activation algorithms in discrete event systems to activate/deactivate sensors for colecting information when it is only necessary to automatically operate headlights based on trafic rules.The framework developed in this paper forms a basis for automatically activating/deactivating sensors for other components in an automated vehicle in the future.展开更多
Car headlight plastic as a kind of evidence often can be seen in traffic accidents and some other cases.We tested 20 brands of car headlight plastic using gel chromatography.The data were processed using the Statistic...Car headlight plastic as a kind of evidence often can be seen in traffic accidents and some other cases.We tested 20 brands of car headlight plastic using gel chromatography.The data were processed using the Statistical Package for the Social Sciences(SPSS)one‑way analysis of variance(ANOVA)and the discrimination rate was 97.14%.This indicated that we could discriminate between different headlights by the molecular weight of their headlight plastic.Gel permeation chromatography is an effective method of discriminating between headlights,particularly in the case of a traffic accident.展开更多
文摘An important and challenging aspect of developing an intelligent transportation system is the identification of nighttime vehicles. Most accidents occur at night owing to the absence of night lighting conditions. Vehicle detection has become a vital subject for research to ensure safety and avoid accidents. New vision-based on-road nighttime vehicle detection and tracking system are suggested in this survey paper using taillight and headlight features. Using computer vision and some image processing techniques, the proposed system can identify vehicles based on taillight and headlight features. For vehicle tracking, a centroid tracking algorithm has been used. Euclidean Distance method has been used for measuring the distances between two neighboring objects and tracks the nearest neighbor. In the proposed system two flexible fixed Region of Interest (ROI) have been used, one is the Headlight ROI, and another is the Taillight ROI that could adapt to different resolutions of the images and videos. The achievement of this research work is that the proposed two ROIs can work simultaneously in a frame to identify oncoming and preceding vehicles at night. The segmentation techniques and double thresholding method have been used to extract the red and white components from the scene to identify the vehicle headlights and taillights. To evaluate the capability of the proposed process, two types of datasets have been used. Experimental findings indicate that the performance of the proposed technique is reliable and effective in distinct nighttime environments for detection and tracking of vehicles. The proposed method has been able to detect and track double lights as well as single light such as motorcycle light and achieved average accuracy and average processing time of vehicle detection about 97.22% and 0.01 s per frame respectively.
基金This work was supported in part by the PowerChina Grant(No.KY2018-JT-20-01-2019)the National Natural Science Foundation of China(No.CNSF-61374058)the Australian Research Council(No.DP-130100156).
文摘The controller in an automated vehicle relies on sensors to collect the information needed for handling traffic situations,and reducing the frequency of using sensors could prolong theirlifespans.We present in this paper the application of dynamic sensor activation algorithms in discrete event systems to activate/deactivate sensors for colecting information when it is only necessary to automatically operate headlights based on trafic rules.The framework developed in this paper forms a basis for automatically activating/deactivating sensors for other components in an automated vehicle in the future.
文摘Car headlight plastic as a kind of evidence often can be seen in traffic accidents and some other cases.We tested 20 brands of car headlight plastic using gel chromatography.The data were processed using the Statistical Package for the Social Sciences(SPSS)one‑way analysis of variance(ANOVA)and the discrimination rate was 97.14%.This indicated that we could discriminate between different headlights by the molecular weight of their headlight plastic.Gel permeation chromatography is an effective method of discriminating between headlights,particularly in the case of a traffic accident.
