Electric vehicles are progressively introduced in urban areas, because of their ability to reduce air pollution, fuel consumption and noise nuisance. Nowadays, some big cities are launching the first electric car-sharing projects to clear traffic jams and enhance urban mobility, as an alternative to the classic public transportation systems. However, there are still some problems to be solved related to energy storage, electric charging and autonomy. In this paper, we present an autonomous docking system for electric vehicles recharging based on an embarked infrared camera performing infrared beacons detection installed in the infrastructure. A visual servoing system coupled with an automatic controller allows the vehicle to dock accurately to the recharging booth in a street parking area. The results show good behavior of the implemented system, which is currently deployed as a real prototype system in the city of Paris.

Vehicle surrounding environment perception is an important process for many applications. Nowadays, a tendency is to incorporate redundant and complementary sensors into an intelligent vehicle, in order to enhance its perception ability; then an essential issue arises naturally, i.e. what fusion architecture can be used to combine the data from multiple sensors? In this paper, we propose a new track-totrack fusion architecture using the split covariance intersection filter-information matrix filter (SCIF-IMF). The basic idea is to use the IMF (adapted for estimates in split form) to handle the track temporal correlation of each sensor system and to use the SCIF to handle track spatial correlation. The proposed architecture enjoys complete sensor modularity and thus enables flexible self-adjustment. A simulation based comparative study is presented, which shows that the track-totrack fusion architecture using the SCIF-IMF can achieve centralized architecture comparable performance.

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We present an evolution of traditional occupancy grid algorithm, based on an extensive probabilistic calculus of the evolution of several variables on a cell neighbourhood. Occupancy, speed and classification are taken into account, the aim being to improve overall perception of an highly changing unstructured environment. Contrary to classical SLAM algorithms, no requisite is made on the amount of rigidity of the scene, and tracking do not rely on geometrical characteristics. We believe that this could have important applications in the automotive field, both from autonomous vehicle and driver assistance, in some areas difficult to address with current algorithms. This article begins with a general presentation of what we aim to do, along with considerations over traditional occupancy grids limits and their reasons. We will then present our proposition, and detail some of its key aspects, namely update rules and performance consequences. A second part will be more practical, and will begin with a brief presentation of the GPU implementation of the algorithm, before turning to sensor models and some results.

International audienceWe present a novel method for scene reconstruction and moving object detection and tracking, using extensive point tracking (typically more than 4000 points per frame) over time. Current neighbourhood is reconstructed in the form of a 3D point cloud, which allows for extra features (ground detection, path planning, obstacle detection). Reconstruction framework takes moving objects into account, and tracking over time allows for trajectory and speed estimation