Social and Hydrological Responses to Extreme Precipitations: An Interdisciplinary Strategy for Postflood Investigation

International audienceThis paper describes and illustrates a methodology to conduct postflood investigations based on interdisciplinary collaboration between social and physical scientists. The method, designed to explore the link between crisis behavioral response and hydrometeorological dynamics, aims at understanding the spatial and temporal capacities and constraints on human behaviors in fast-evolving hydrometeorological conditions. It builds on methods coming from both geosciences and transportations studies to complement existing post-flood field investigation methodology used by hydrometeorologists. The authors propose an interview framework, structured around a chronological guideline to allow people who experienced the flood firsthand to tell the stories of the circumstances in which their activities were affected during the flash flood. This paper applies the data collection method to the case of the 15 June 2010 flash flood event that killed 26 people in the Draguignan area (Var, France). As a first step, based on the collected narratives, an abductive approach allowed the identification of the possible factors influencing individual responses to flash floods. As a second step, behavioral responses were classified into categories of activities based on the respondents' narratives. Then, aspatial and temporal analysis of the sequences made of the categories of action to contextualize the set of coping responses with respect to local hydrometeorological conditions is proposed. During this event, the respondents mostly follow the pace of change in their local environmental conditions as the flash flood occurs, official flood anticipation being rather limited and based on a large-scale weather watch. Therefore, contextual factors appear as strongly influencing the individual's ability to cope with the event in such a situation

Feature-refined box particle filtering for autonomous vehicle localisation with OpenStreetMap

Vehicle localisation is an important and challenging task in achieving autonomous driving. This work presents a box particle filter framework for vehicle self-localisation in the presence of sensor and map uncertainties. The proposed feature-refined box particle filter incorporates line features extracted from a multi-layer Light Detection And Ranging (LiDAR) sensor and information from OpenStreetMap to estimate vehicle states. A particle weight balance strategy is incorporated to account for the OpenStreetMap positional inaccuracy, which is assessed by comparing it to a high definition road map. The performance of the proposed framework is evaluated on a LiDAR dataset and compared with box particle filter variants. Experimental results show that the proposed framework achieves respectively 10% and 53% localisation performance improvement with reduced box volumes of 25% and 41%, when compared with the state-of-the-art interval analysis based box regularisation particle filter and the box particle filter