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International audience; Given the boom linked to smart mobility, transport systems require increasingly precise and relevant navigation applications to offer optimized journeys in terms of time and energy consumption, such as for HEV. Most of these navigation applications are based on the processing of 2D digital road maps, while taking into account the GNSS location of vehicles. These localization systems also integrate sensors such as accelerometers and gyroscopes to overcome the well-known problems of GPS positioning, even if the current limited introduction of IoT in the transport industry has made it possible to develop new aided-GPS methods such as geofencing. This paper focuses on one important parameter in the journey optimization of land vehicles: the road slope. We propose a method to estimate the roads’ inclination parameters by fusing GNSS, INS, OSM and ASTER GDEM data through a nonlinear filter. The incremental estimate of the slope will complement the 2D modeling of the roads already available in OpenStreetMap and could be used in route planning optimization. The scientific novelty lies more specifically in the statistical map-matching approaches that we develop both for OSM and DEM data. Estimation results of the roads slopes are shown in experimental conditions.

The objective of this work is to estimate the localization and attitude of a land-vehicle by fusing GPS, OSM and DEM data through a nonlinear filter. We focus on the heading and pitch angles of the vehicle, knowing that these parameters are essential in the optimization of the route planning and energy management for an EV. This paper investigates the performance of particle filtering and probabilistic map-matching algorithms for tracking a vehicle with the help of digital roadmaps to improve the ground-location. Also, the filter fuses DEM data through a TIN method in order to bound altitude errors caused by GPS. The proposed method is evaluated through an urban transport network scenario and experimental results show that the proposed estimator can accurately estimate the vehicle location and attitude.

As people age, they become more fragile and exhibit difficulties in maintaining their gait and balance. Their state of fragility increases their vulnerability to fall incidents. Various analysis methods were developed to detect the abnormality of human gait and balance, and estimate the risk of falling. In this paper, we present a method to estimate the falling risk and alert the patient when a fall is about to happen. The proposed method consists in monitoring and analyzing the amount of sway of the center of mass in the medial-lateral plane by computing the center of pressure displacement at the foot plantar surface. Our proposed method uses the spatio-temporal sliding window processing to generate fall alarms and estimate the falling risk. The method was validated via a two-phase experimental protocol with five young adults who performed a walk of 20 stances with simulated sways using an instrumented shoe with resistive pressure sensors. The threshold of the normal walk TH N and the risk level R L of the altered walk are determined as well as the risk of falling. The method can be applied in real-life and clinical settings with real-time processing.