Current Issue : October - December Volume : 2011 Issue Number : 1 Articles : 5 Articles
The important innovations in wireless and digital electronics will support many applications in the areas of safety, environmental and emissions control, driving assistance, diagnostics, and maintenance in the transport domain. The last few years have seen the emergence of many new technologies that can potentially have major impacts on transportation systems. One of these technologies is Wireless Sensor Networks. A wireless sensor device is typically composed of a processing unit, memory, and a radio chip which allows it to communicate wirelessly with other devices within range. The Embedded Middleware in Mobility Applications (EMMA) project delivers a middleware that aims to facilitate the interaction between sensing technologies in transportation systems. This paper outlines our experience in the EMMA project and provides an illustration of the important role that wireless sensor technology can play in future transportation system. The paper discusses our experience of using heterogeneous sensors to develop transportation system applications in the EMMA project and focuses on how cooperation between vehicle and infrastructure can be addressed. It also presents encouraging results obtained from the experiments in investigating the feasibility of utilising wireless sensor in vehicle and vehicle-to-infrastructure communication in real transportation applications....
With transport and traffic developing permanently, we can meet more and more aggressive drivers on roads. We can see various kinds of aggressiveness and aggressive behavior that can lead to dangerous situations which can threaten one's health or even life. The problem of aggressive driving on the roads is becoming more current. Speeding, inappropriate gestures, and nonobservance of safe distance, are only a fraction of the aggressive behavior of many drivers that need to be solved in the road traffic. At present, the problem of aggressive driver behavior in Slovakia is not resolved yet....
A new privacy model for Location-Based Services (LBSs) has been recently proposed based on users' footprints-these being a repre-sentation of the amount of time a user spends in a given area. Unfortunately, while the model is claimed to be independent from the specific knowledge of the adversary about users' footprints, we argue that an adversary, that has a more structured knowledge over time, can pose a threat to the privacy guarantees of the model. The major contribution of this paper is to show that time is a relevant dimension that needs to be taken into consideration when investigating LBSs privacy issues. In particular, we show that applying our considerations, user privacy can be violated. We support our claim with analysis and a concrete example. Furthermore, by analyzing a real data set of vehicular traces, we show that the threat is actually present in a real scenario and that its effect on jeopardizing user privacy is relevant....
In this work a methodology for detecting drivers' stress and fatigue and predicting driving performance is presented. The proposed methodology exploits a set of features obtained from three different sources: (i) physiological signals from the driver (ECG, EDA, and respiration), (ii) video recordings from the driver's face, and (iii) environmental information. The extracted features are examined in terms of their contribution to the classification of the states under investigation. The most significant indicators are selected and used for classification using various classifiers. The approach has been validated on an annotated dataset collected during real-world driving. The results obtained from the combination of physiological signals, video features, and driving environment parameters indicate high classification accuracy (88% using three fatigue scales and 86% using two stress scales). A series of experiments on a simulation environment confirms the association of fatigue states with driving performance....
Tracking the actions of vehicles at crossroads and planning safe trajectories will be an effective method to reduce the rate of traffic accident at intersections. It is to resolve the problem of the abrupt change because of the existence of drivers' voluntary choices. In this paper, we make approach of an improved IMM tracking method based on trajectory generation, abstracted by trajectory generation algorithm, to improve this situation. Because of the similarity between human-driving trajectory and programming trajectory which is generated by trajectory-generated algorithm, the improved IMM method performs well in tracking moving vehicles with some sudden changes of its movement. A set of data is collected for experiments when an object vehicle takes a sudden left turn in intersection scenario. To compare the experiment results between IMM method with trajectory generation model and the one without, tracking error of the former decreases by 75% in particular scenario....
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