This paper presents the implementation of an embedded automotive system that detects and recognizes traffic signs within a video stream. In addition, it discusses the recent advances in driver assistance technologies ...This paper presents the implementation of an embedded automotive system that detects and recognizes traffic signs within a video stream. In addition, it discusses the recent advances in driver assistance technologies and highlights the safety motivations for smart in-car embedded systems. An algorithm is presented that processes RGB image data, extracts relevant pixels, filters the image, labels prospective traffic signs and evaluates them against template traffic sign images. A reconfigurable hardware system is described which uses the Virtex-5 Xilinx FPGA and hardware/software co-design tools in order to create an embedded processor and the necessary hardware IP peripherals. The implementation is shown to have robust performance results, both in terms of timing and accuracy.展开更多
Study of the impact of traffic emissions on air quality around the Haram Mosque in Makkah, Saudi Arabia, was conducted experimentally, numerically and statistically. Experimental study was performed to measure existin...Study of the impact of traffic emissions on air quality around the Haram Mosque in Makkah, Saudi Arabia, was conducted experimentally, numerically and statistically. Experimental study was performed to measure existing air quality. Numerical study was done to model the extent of air movement and pollutant dispersion within and around the Haram area. Statistical study was conducted to determine correlation coefficients, auto-correlation and time lags of each pollutant. Pollutant measurements were carried out using an air quality mobile laboratory at three sites. Numerical calculations were made using an ISC-AERMOD dispersion model. Concentrations of traffic emissions including nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), ozone (O3) and airborne particulate material under 10 μm diameter (PM10) are presented and analyzed. The calculated concentrations are validated by comparing with observed values at the three sites. The results indicate good agreement between calculated concentrations and observed values, which demonstrate satisfactory model performance. Results show that the Haram area is experiencing high concentrations of dust. High buildings around the Haram Mosque act as flow obstacles. Mean pollutant dispersion was toward the south and southeast during January and June. Highest mean concentrations were observed in January and June.展开更多
To reduce the computation cost of a combined probabilistic graphical model and a deep neural network in semantic segmentation, the local region condition random field (LRCRF) model is investigated which selectively ap...To reduce the computation cost of a combined probabilistic graphical model and a deep neural network in semantic segmentation, the local region condition random field (LRCRF) model is investigated which selectively applies the condition random field (CRF) to the most active region in the image. The full convolutional network structure is optimized with the ResNet-18 structure and dilated convolution to expand the receptive field. The tracking networks are also improved based on SiameseFC by considering the frame relations in consecutive-frame traffic scene maps. Moreover, the segmentation results of the greyscale input data sets are more stable and effective than using the RGB images for deep neural network feature extraction. The experimental results show that the proposed method takes advantage of the image features directly and achieves good real-time performance and high segmentation accuracy.展开更多
目前的行车安全场理论基于“人-车-路”三维构建势能函数,但忽略了复杂的天气因素对行车风险的复合影响,将道路状况(“路”)与天气情况(“环”)所产生的影响简单地归于一类。这低估了天气环境对行车风险的影响程度,并存在对极端天气的...目前的行车安全场理论基于“人-车-路”三维构建势能函数,但忽略了复杂的天气因素对行车风险的复合影响,将道路状况(“路”)与天气情况(“环”)所产生的影响简单地归于一类。这低估了天气环境对行车风险的影响程度,并存在对极端天气的风险解算不够敏感的问题,使得方法的实际应用受到较大局限。因此基于行车安全场(driving safety field,DSF)理论,引入了新的环境场函数,实现“人-车-路-环”的风险因素全覆盖,分别构建行为场(behavior field)、动能场(kinetic energy field)、势能场(potential energy field)和环境场(environment field),以此提出针对恶劣天气下的行车安全场BKPE模型。基于中国道路交通安全数据集对原有行车安全场相关参数进行重新标定。同时分析天气因素对行车安全影响的指数变化特征,构建环境了影响因子,并提出环境场函数。在构建包含环境场的行车安全场模型的基础上,基于Car-100数据集,对具体实例计算其人工势能函数,进行微观分析。通过2个典型事件进行多类型风险的量化分析,同时与原有行车安全场模型进行比较分析,说明原有行车安全场模型对于天气环境形成的风险存在低估。随后基于Bootstrap抽样,6次采样计算所得人工势能函数对实际交通事件的描述平均准确率达到91.7%。最终,基于BKPE模型,提出相应的行车风险控制对策。展开更多
文摘This paper presents the implementation of an embedded automotive system that detects and recognizes traffic signs within a video stream. In addition, it discusses the recent advances in driver assistance technologies and highlights the safety motivations for smart in-car embedded systems. An algorithm is presented that processes RGB image data, extracts relevant pixels, filters the image, labels prospective traffic signs and evaluates them against template traffic sign images. A reconfigurable hardware system is described which uses the Virtex-5 Xilinx FPGA and hardware/software co-design tools in order to create an embedded processor and the necessary hardware IP peripherals. The implementation is shown to have robust performance results, both in terms of timing and accuracy.
文摘Study of the impact of traffic emissions on air quality around the Haram Mosque in Makkah, Saudi Arabia, was conducted experimentally, numerically and statistically. Experimental study was performed to measure existing air quality. Numerical study was done to model the extent of air movement and pollutant dispersion within and around the Haram area. Statistical study was conducted to determine correlation coefficients, auto-correlation and time lags of each pollutant. Pollutant measurements were carried out using an air quality mobile laboratory at three sites. Numerical calculations were made using an ISC-AERMOD dispersion model. Concentrations of traffic emissions including nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), ozone (O3) and airborne particulate material under 10 μm diameter (PM10) are presented and analyzed. The calculated concentrations are validated by comparing with observed values at the three sites. The results indicate good agreement between calculated concentrations and observed values, which demonstrate satisfactory model performance. Results show that the Haram area is experiencing high concentrations of dust. High buildings around the Haram Mosque act as flow obstacles. Mean pollutant dispersion was toward the south and southeast during January and June. Highest mean concentrations were observed in January and June.
文摘To reduce the computation cost of a combined probabilistic graphical model and a deep neural network in semantic segmentation, the local region condition random field (LRCRF) model is investigated which selectively applies the condition random field (CRF) to the most active region in the image. The full convolutional network structure is optimized with the ResNet-18 structure and dilated convolution to expand the receptive field. The tracking networks are also improved based on SiameseFC by considering the frame relations in consecutive-frame traffic scene maps. Moreover, the segmentation results of the greyscale input data sets are more stable and effective than using the RGB images for deep neural network feature extraction. The experimental results show that the proposed method takes advantage of the image features directly and achieves good real-time performance and high segmentation accuracy.
文摘目前的行车安全场理论基于“人-车-路”三维构建势能函数,但忽略了复杂的天气因素对行车风险的复合影响,将道路状况(“路”)与天气情况(“环”)所产生的影响简单地归于一类。这低估了天气环境对行车风险的影响程度,并存在对极端天气的风险解算不够敏感的问题,使得方法的实际应用受到较大局限。因此基于行车安全场(driving safety field,DSF)理论,引入了新的环境场函数,实现“人-车-路-环”的风险因素全覆盖,分别构建行为场(behavior field)、动能场(kinetic energy field)、势能场(potential energy field)和环境场(environment field),以此提出针对恶劣天气下的行车安全场BKPE模型。基于中国道路交通安全数据集对原有行车安全场相关参数进行重新标定。同时分析天气因素对行车安全影响的指数变化特征,构建环境了影响因子,并提出环境场函数。在构建包含环境场的行车安全场模型的基础上,基于Car-100数据集,对具体实例计算其人工势能函数,进行微观分析。通过2个典型事件进行多类型风险的量化分析,同时与原有行车安全场模型进行比较分析,说明原有行车安全场模型对于天气环境形成的风险存在低估。随后基于Bootstrap抽样,6次采样计算所得人工势能函数对实际交通事件的描述平均准确率达到91.7%。最终,基于BKPE模型,提出相应的行车风险控制对策。
