No abstract

Context. Until recently, camera networks designed for monitoring fireballs worldwide were not fully automated, implying that in case of a meteorite fall, the recovery campaign was rarely immediate. This was an important limiting factor as the most fragile – hence precious – meteorites must be recovered rapidly to avoid their alteration. Aims. The Fireball Recovery and InterPlanetary Observation Network (FRIPON) scientific project was designed to overcome this limitation. This network comprises a fully automated camera and radio network deployed over a significant fraction of western Europe and a small fraction of Canada. As of today, it consists of 150 cameras and 25 European radio receivers and covers an area of about 1.5 × 106 km2. Methods. The FRIPON network, fully operational since 2018, has been monitoring meteoroid entries since 2016, thereby allowing the characterization of their dynamical and physical properties. In addition, the level of automation of the network makes it possible to trigger a meteorite recovery campaign only a few hours after it reaches the surface of the Earth. Recovery campaigns are only organized for meteorites with final masses estimated of at least 500 g, which is about one event per year in France. No recovery campaign is organized in the case of smaller final masses on the order of 50 to 100 g, which happens about three times a year; instead, the information is delivered to the local media so that it can reach the inhabitants living in the vicinity of the fall. Results. Nearly 4000 meteoroids have been detected so far and characterized by FRIPON. The distribution of their orbits appears to be bimodal, with a cometary population and a main belt population. Sporadic meteors amount to about 55% of all meteors. A first estimate of the absolute meteoroid flux (mag < –5; meteoroid size ≥~1 cm) amounts to 1250/yr/106 km2. This value is compatible with previous estimates. Finally, the first meteorite was recovered in Italy (Cavezzo, January 2020) thanks to the PRISMA network, a component of the FRIPON science project.

Abstract-Each driver reacts differently to the same traffic conditions, however, most Advanced Driving Assistant Systems (ADAS) assume that all drivers are the same. This paper proposes a method to learn and to model the velocity profile that the driver follows as the vehicle decelerates towards a stop intersection. Gaussian Processes (GP), a machine learning method for non-linear regressions are used to model the velocity profiles. It is shown that GP are well adapted for such an application, using data recorded in real traffic conditions. It consists of the generation of a normally distributed speed, given a position on the road. By comparison with generic velocity profiles, benefits of using individual driver patterns for ADAS issues are presented.

For an autonomous vehicle, situation understanding is a key capability towards safe and comfortable decisionmaking and navigation. Information is in general provided by multiple sources. Prior information about the road topology and traffic laws can be given by a High Definition (HD) map while the perception system provides the description of the space and of road entities evolving in the vehicle surroundings. In complex situations such as those encountered in urban areas, the road user behaviors are governed by strong interactions with the others, and with the road network. In such situations, reliable situation understanding is therefore mandatory to avoid inappropriate decisions. Nevertheless, situation understanding is a complex task that requires access to a consistent and non-misleading representation of the vehicle surroundings. This paper proposes a formalism (an interaction lane grid) which allows to represent, with different levels of abstraction, the navigable and interacting spaces which must be considered for safe navigation. A top-down approach is chosen to assess and characterize the relevant information of the situation. On a high level of abstraction, the identification of the areas of interest where the vehicle should pay attention is depicted. On a lower level, it enables to characterize the spatial information in a unified representation and to infer additional information in occluded areas by reasoning with dynamic objects.This work is carried out within SIVALab, a shared laboratory between Renault and Heudiasyc (UTC/CNRS), and financed by the CNRS.