A Neutral Network Based Vehicle Classification System for Pervasive Smart Road Security

Pervasive smart computing environments make people get accustomed to convenient and secure services. The overall goal of this research is to classify vehicles along the I215 freeway in Salt Lake City, USA. This information will be used to predict future roadway needs and the expected life of a roadway. The classification of vehicles will be performed by a synthesis of multiple sets of features. All feature sets have not yet been determined; however, one such set will be the reduced wavelet transform of the image of a vehicle. In order to use such a feature, it is necessary that the image be normalized with respect to size, position, and so on. For example, a car in the right most lane in an image will appear smaller than one in the left most lane, because the right most lane is closest to the camera. Likewise, a vehicle’s size will vary depending on where in a lane its image is captured. In our case, the image capture area for each lane is approximately 100 feet of roadway. A goal of this paper is to normalize the image of a vehicle so that regardless of its lane or position in a lane, the features will be approximately the same. The wavelet transform itself will not be used directly for recognition. Instead, it will be input to a neural network and the output of the neural network will be one element of the feature set used for recognition

GAFU: Using a gamification tool to save fuel

In this paper, we propose, implement and user-validate a training tool for saving fuel that uses some elements from games in order to promote efficient driving and provide feedback to the user. The proposed system uses a fuzzy logic system in order to assess the driving style from the point of view of the fuel consumption. The output is a score between 0 (not efficient) and 10 (efficient). This value can be compared with the scores obtained by other users of the solution that have similar characteristics in order to do a fair comparison and to obtain eco-driving advices adapted to the user's context and environment (e.g., braking frequency is greater on urban road than highway). Providing feedback to the user is essential in eco-driving systems for changing bad driving habits and not returning back to them. In our case, the system provides two types of feedback. The first type of feedback is provided in real time. When the user does not comply with some of a preconfigured set of eco-driving rules, he or she gets a warning message. The second type of feedback is based on a calculated relative score for each user according to his or her driving style, positioning the user into a ranking of eco-driving users and generating a set of eco-driving tips. A validation experiment has been conducted with 36 participants on three different routes in Spain. The results show that the use of gamification tools and techniques in eco-driving assistants helps drivers not to lose interest for fuel saving and helps them not to return back to their previous bad driving habits.The research leading to these results has received fund-ing from the “HERMES-SMART DRIVER” project TIN2013- 46801-C4-2-R within the Spanish “Plan Nacional de I+D+I” under the Spanish Ministerio de Economía y Competitividad and from the Spanish Ministerio de Economía y Competi-tividad funded projects (co-financed by the Fondo Europeo de Desarrollo Regional (FEDER)) IRENE (PT-2012-1036- 370000), COMINN (IPT-2012-0883-430000) and REMEDISS (IPT-2012-0882-430000) within the INNPACTO program.Publicad

A Neural Network Based Vehicle Classification System for Pervasive Smart Road Security

Pervasive smart computing environments make people get accustomed to convenient and secure services. The overall goal of this research is to classify vehicles along the I215 freeway in Salt Lake City, USA. This information will be used to predict future roadway needs and the expected life of a roadway. The classification of vehicles will be performed by a synthesis of multiple sets of features. All feature sets have not yet been determined; however, one such set will be the reduced wavelet transform of the image of a vehicle. In order to use such a feature, it is necessary that the image be normalized with respect to size, position, and so on. For example, a car in the right most lane in an image will appear smaller than one in the left most lane, because the right most lane is closest to the camera. Likewise, a vehicle’s size will vary depending on where in a lane its image is captured. In our case, the image capture area for each lane is approximately 100 feet of roadway. A goal of this paper is to normalize the image of a vehicle so that regardless of its lane or position in a lane, the features will be approximately the same. The wavelet transform itself will not be used directly for recognition. Instead, it will be input to a neural network and the output of the neural network will be one element of the feature set used for recognition