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Vehicle Logo Recognition(VLR) has been an important study field in intelligent Transportation system (ITS). This paper proposes to recognize vehicle logo and predict logo attributes by combining Convolutional Neural Network (CNN) with Multi-Task Learning(MTL). In order to accelerate convergence of multi-task model, an adaptive weight training strategy is employed. To verify the algorithm, the Xiamen...
Data mining technology is an interdiscipline using theory and technology of artificial intelligence, machine learning, statistics and other fields. It can extract implicit but useful information and knowledge from vast amount of historical data for the enterprise, and provide solid support for the decision of company. Combining with the rate reform of domestic automobile insurance industry, this paper...
The emissions of on-road vehicles are studied based on a remote sensing system and artificial neural network models. A transportable vehicle emission remote sensing system is used to collect the emission data from May to August 2012 in Hefei, China. Based on these light-duty gasoline vehicle data containing the emission pollutants such as carbon monoxide, hydrocarbons, nitric oxide, and so on, artificial...
An efficient recognition framework requires both good feature representation and effective classification methods. This paper proposes such a framework based on a spatial Scale Invariant Feature Transform (SIFT) combined with a logistic regression classifier. The performance of the proposed framework is compared to that of state-of-the-art methods based on the Histogram of Orientation Gradients, SIFT...
The construction industry accounts for a high number of accidents. Although identifying hazards before construction starts or during construction is widely employed to prevent accidents, it typically fails because of insufficient safety experience. The experience helps in training novice inspectors, although extracting and describing tacit knowledge explicitly is difficult. This study created a 3-D...
As an application of image recognition, special vehicle recognition is very important in military field. This paper proposes a deep-transfer model (DTM) to overcome the problems in existing recognition methods. The DTM combines deep-learning and transfer-learning to solve the difficulty in training deep model with insufficient simples, improving the performance of the recognition algorithm. At last,...
Advanced map-matching algorithms use location and heading of GPS points along with geometrical and topological features of digital road networks to find the road segment on which the vehicle is moving. However, GPS errors sometimes impede map-matching algorithms in finding the correct segment, especially in dense and complicated parts of the network, such as near intersections with acute angles or...
High accuracy of lane changing prediction is beneficial to driver assistant system and fully autonomous cars. This paper proposes a lane changing prediction model based on combined method of Supporting Vector Machine (SVM) and Artificial Neural Network (ANN) at highway lane drops. The vehicle trajectory data are from Next Generation Simulation (NGSIM) data set on U.S. Highway 101 and Interstate 80...
This paper presents a robust approach to classify the driver state in a novel advanced driver assistance system named ShadeVision, which aims to improve the driving safety and comfort by avoiding the dazzling effect. Different from the existing algorithms for driver state classification, our proposed method is capable of identifying the "dazzled state" of the driver, which can function as...
Road marking is a key visual cue for driving in structured environments like highways and urban roads. Road marking detection plays an important role in advanced driver assistant systems and autonomous driving. Robust road marking detection is challenging for the variation of road scenes, the degradation of the markings and the changes of the illumination. Traditional algorithms mainly use the grayscale...
Modern vehicles are equipped with numerous driver assistance and telematics functions, such as Turn-by-Turn navigation. Most of these systems rely on precise positioning of the vehicle. While Global Navigation Satellite Systems (GNSS) are available outdoors, these systems fail in indoor environments such as a car-park or a tunnel. Alternatively, the vehicle can localize itself with landmark-based...
This paper introduces the reasoning and concepts behind a generic and modular approach to obstacle avoidance based on geofencing. The issue of UAV safety is tackled from a practical point of view and a collision avoidance system based on practical usage and safety considerations is presented. Before doing so, the need for a reference model of geographic geometry is discussed, looking at global positioning...
The efficient condition assessment of road networks is crucial to prevent pavement distresses which can cause a spectrum of detrimental effects. The need for automation of the underlying process is originated from the costly, time-consuming and dangerous current methods. Presented herein is the automation of the patch detection process, which is essential for pavement surface evaluation and rating...
The present paper shows the current status of the student hands-on project eCARus which has been offered at the Technical University of Munich (TUM) for already more than six years. Within this paper, the project and team structure are explained. Furthermore, the two electric vehicles, called eCARus 1.0 and eCARus 2.0, developed by the students are presented. Both vehicles are continuously expanded...
In 2014, funded by the European Commission through the Marie Curie Programme, ten leading European research institutes and companies in underwater robotics formed the ROBOCADEMY Initial Training Network (ITN). The objective of the network is to educate young researchers from Europe and abroad in the development and application of underwater robots. The ROBOCADEMY training programme comprises of scientific...
Vehicular ad hoc network facilitates cooperative awareness applications by periodically sharing basic safety messages (BSMs) with the neighborhood vehicles. As these applications impact human safety, authenticity of BSMs is a key requirement, which however is a challenging task in a dense traffic density where many BSMs are queued up simultaneously for signature verification. As a result, many important...
High Occupancy Vehicle (HOV) and High Occupancy Tolling (HOT) lanes have been commonly practiced in several jurisdictions to reduce traffic congestion and promote car pooling. Camera-based methods have been recently proposed for a cost-efficient, safe and effective HOV/HOT lane enforcement with the prevalence of video cameras in transportation imaging applications. An important step in automated lane...
Fast growth of mobile sensing technologies, like GPS in smart phones, made capturing position data in the form of trajectories easy. But detecting anomalous trajectories, which are grossly different from remaining trajectories, is a major challenge in the surveillance domain and a big data problem. In the present work, a novel density-based method has been proposed and implemented for detection of...
Epifanio de los Santos Avenue (EDSA) is one of the busiest national road in the Philippines millions vehicle are passing thru it every day especially in rush hour. Implementing Intelligent Transportation System (ITS) along this high way will provide a big help to every Filipino. This paper applied Artificial Intelligent (AI) and Artificial Neural Networks (ANN) to find the corresponding bus schedule...
The influence from individual preference and circumstance has been considered in the distributed path-planning model which is generated by Maximum Likelihood Estimation (MLE) and Bayesian Theory, and the model parameter can be determined with actual driving records of an optimal path-planning model. The new model develops the previous one from a single path into a distribution based on driving records...
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