The present paper aims to describe the conceptual idea to use cars as sensors to measure and acquire data related road environment. The parameters are collected using only standard equipment commonly installed and ope...The present paper aims to describe the conceptual idea to use cars as sensors to measure and acquire data related road environment. The parameters are collected using only standard equipment commonly installed and operative on commercial cars. Real sensors and car sub-systems (e.g. thermometers, accelerometers, ABS, ESP, and GPS) together with other “implicit” sensors (e.g. fog lights, windscreen wipers) acquire and contain information. They are shared inside an in-vehicle communication network using mainly the standard CAN bus and can be collected by a simple central node. This node can also be available on the market without too expensive costs thanks to some companies which business is devoted to car fleet monitoring. All the collected data are then geolocalized using a standard GPS receiver and sent to a remote elaboration unit, exploiting mobile network technologies such as GPRS or UMTS. A large number of cars, connected together in a diffuse Wireless Sensor Network, allow the elaboration unit to realize some info-layers put at the disposal of a car driver. Traffic, state of the road and other information about the weather can be received by car drivers using an ad hoc developed mobile application for smartphone which can give punctual information related to a specific route, previously set on the mobile phone navigator. The description of some experimental activities is presented, some technical points will be addressed and some examples of applications of the network of cars “as sensors” will be given.展开更多
针对目前路径跟踪控制检测方法精度低、实时性能差的问题,提出一种基于深度机器学习的构建树形神经网络CTNN(Constructing Tree shaped Neural Net)的深度学习算法。该算法通过深度机器学习,构建针对性强的学习集,同时在模型车中实现。...针对目前路径跟踪控制检测方法精度低、实时性能差的问题,提出一种基于深度机器学习的构建树形神经网络CTNN(Constructing Tree shaped Neural Net)的深度学习算法。该算法通过深度机器学习,构建针对性强的学习集,同时在模型车中实现。将传统机器学习算法与文章所提出的算法在相同行驶条件下的实时响应进行比较,仿真结果表明,CTNN算法在恶劣的行驶环境中,实时性、鲁棒性均得到一定程度的提高。展开更多
车辆型号识别在智能交通系统、涉车刑侦案件侦破等方面具有十分重要的应用前景.针对车辆型号种类繁多、部分型号区分度小等带来的车辆型号精细分类困难的问题,采用车辆正脸图像为数据源,提出一种多分支多维度特征融合的卷积神经网络模型...车辆型号识别在智能交通系统、涉车刑侦案件侦破等方面具有十分重要的应用前景.针对车辆型号种类繁多、部分型号区分度小等带来的车辆型号精细分类困难的问题,采用车辆正脸图像为数据源,提出一种多分支多维度特征融合的卷积神经网络模型Fg-CarNet (Convolutional neural networks for car fine-grained classification, Fg-CarNet).该模型根据车正脸图像特征分布特点,将其分为上下两部分并行进行特征提取,并对网络中间层产生的特征进行两个维度的融合,以提取有区分度的特征,提高特征表达能力,通过使用小卷积核以及全局均值池化,使在网络分类准确度提高的同时降低了网络模型参数大小.在CompCars数据集上进行验证,实验结果表明, Fg-CarNet提取的车辆特征在保证网络模型参数最小的同时,车辆型号识别率达到最高,实现了最好的分类效果.展开更多
We introduce an almost-automatic technique for generating 3D car styling surface models based on a single side-view image. Our approach combines the prior knowledge of car styling and deformable curve network model to...We introduce an almost-automatic technique for generating 3D car styling surface models based on a single side-view image. Our approach combines the prior knowledge of car styling and deformable curve network model to obtain an automatic modeling process. Firstly, we define the consistent parameterized curve template for 2D and 3D case respectivelyby analyzingthe characteristic lines for car styling. Then, a semi-automatic extraction from a side-view car image is adopted. Thirdly, statistic morphable model of 3D curve network isused to get the initial solution with sparse point constraints.Withonly afew post-processing operations, the optimized curve network models for creating surfaces are obtained. Finally, the styling surfaces are automatically generated using template-based parametric surface modeling method. More than 50 3D curve network models are constructed as the morphable database. We show that this intelligent modeling toolsimplifiesthe exhausted modeling task, and also demonstratemeaningful results of our approach.展开更多
文摘The present paper aims to describe the conceptual idea to use cars as sensors to measure and acquire data related road environment. The parameters are collected using only standard equipment commonly installed and operative on commercial cars. Real sensors and car sub-systems (e.g. thermometers, accelerometers, ABS, ESP, and GPS) together with other “implicit” sensors (e.g. fog lights, windscreen wipers) acquire and contain information. They are shared inside an in-vehicle communication network using mainly the standard CAN bus and can be collected by a simple central node. This node can also be available on the market without too expensive costs thanks to some companies which business is devoted to car fleet monitoring. All the collected data are then geolocalized using a standard GPS receiver and sent to a remote elaboration unit, exploiting mobile network technologies such as GPRS or UMTS. A large number of cars, connected together in a diffuse Wireless Sensor Network, allow the elaboration unit to realize some info-layers put at the disposal of a car driver. Traffic, state of the road and other information about the weather can be received by car drivers using an ad hoc developed mobile application for smartphone which can give punctual information related to a specific route, previously set on the mobile phone navigator. The description of some experimental activities is presented, some technical points will be addressed and some examples of applications of the network of cars “as sensors” will be given.
文摘针对目前路径跟踪控制检测方法精度低、实时性能差的问题,提出一种基于深度机器学习的构建树形神经网络CTNN(Constructing Tree shaped Neural Net)的深度学习算法。该算法通过深度机器学习,构建针对性强的学习集,同时在模型车中实现。将传统机器学习算法与文章所提出的算法在相同行驶条件下的实时响应进行比较,仿真结果表明,CTNN算法在恶劣的行驶环境中,实时性、鲁棒性均得到一定程度的提高。
文摘车辆型号识别在智能交通系统、涉车刑侦案件侦破等方面具有十分重要的应用前景.针对车辆型号种类繁多、部分型号区分度小等带来的车辆型号精细分类困难的问题,采用车辆正脸图像为数据源,提出一种多分支多维度特征融合的卷积神经网络模型Fg-CarNet (Convolutional neural networks for car fine-grained classification, Fg-CarNet).该模型根据车正脸图像特征分布特点,将其分为上下两部分并行进行特征提取,并对网络中间层产生的特征进行两个维度的融合,以提取有区分度的特征,提高特征表达能力,通过使用小卷积核以及全局均值池化,使在网络分类准确度提高的同时降低了网络模型参数大小.在CompCars数据集上进行验证,实验结果表明, Fg-CarNet提取的车辆特征在保证网络模型参数最小的同时,车辆型号识别率达到最高,实现了最好的分类效果.
基金Supported by National Natural Science Foundation of China(Nos.11472073,61173102 and 61370143)
文摘We introduce an almost-automatic technique for generating 3D car styling surface models based on a single side-view image. Our approach combines the prior knowledge of car styling and deformable curve network model to obtain an automatic modeling process. Firstly, we define the consistent parameterized curve template for 2D and 3D case respectivelyby analyzingthe characteristic lines for car styling. Then, a semi-automatic extraction from a side-view car image is adopted. Thirdly, statistic morphable model of 3D curve network isused to get the initial solution with sparse point constraints.Withonly afew post-processing operations, the optimized curve network models for creating surfaces are obtained. Finally, the styling surfaces are automatically generated using template-based parametric surface modeling method. More than 50 3D curve network models are constructed as the morphable database. We show that this intelligent modeling toolsimplifiesthe exhausted modeling task, and also demonstratemeaningful results of our approach.
