Electric vehicles are currently a field of research with many challenges that raise the interest of many researchers. The major challenge is about the autonomy of electric vehicles, which is limited as compared to that of conventional vehicles, and thus the drivers' anxiety about reaching or not the desired destination is very important. In this paper, we propose an experimental study to understand the energy consumption of electric vehicles, and we investigate some factors that have an important impact on their autonomy, such as the route type, the driving style and the ambient temperature. This study can be very useful in a further step to conceive a driving assistance system that indicates in real time the remaining energy and gives online instructions to reach the next charging station or the final destination. The analysis reported in this paper is based on real-world data collected using a full electric car with different driving conditions.

This paper introduces an online pedestrian crossing detection system that uses pre-existing traffic-oriented video-sensors which, at regular intervals, provide coarse spatial measurements on areas along a crosswalk. Pedestrian crossing detection is based on the recognition of occupancy patterns induced by pedestrians when they move on the crosswalk. In order to improve the ability of non-dedicated sensors to detect pedestrians, we introduce an evidential-based data fusion process that exploits redundant information coming from one or two sensors: intra-sensor fusion uses spatiotemporal characteristics of the measurements, and inter-sensor fusion uses redundancy between the two sensors. As part of the EU funded TRACKSS project on co- http://www.sciencedirect.com/science/journal/0968090X patterns obtained and leads to high detection rates of pedestrian crossings with multi-purpose sensors in operational conditions, especially when a secondary sensor is available.

Online fuzzy expert systems can be used to process data and event streams, providing a powerful way to handle their uncertainties and their inaccuracy. Moreover, human experts can decide how to process the streams with rules close to natural language. However, to extract high level information from these streams, they need at least to describe the temporal relations between the data or the events.In this paper, we propose a straightforward way to design temporal operators which relies on the mathematical definition of some base operators and then their combination into more sophisticated operators to assess precedence, periodicity or persistence. We also introduce the concept of expiration of temporal expressions on online fuzzy expert systems, that is to say the capacity to change the values of outputs whereas the inputs have not changed.

The Internet of Things was born from the proliferation of connected objects and is known as the third era of information technology. It results in the availability of a huge amount of continuously acquired data which need to be processed to be more valuable. This leads to a real paradigm shift: instead of processing fixed data like classical databases or files, the new algorithms have to deal with