Feature mapping and state estimation for highly automated vehicles

The past decade has witnessed an acceleration of autonomous vehicle research and development,with technological advances contributed by academia,government,and the industrial and consumer sectors.These advancements hold the potential to improve society by enhancing transportation safety and throughput,where decreased congestion saves time and reduces vehicle emissions.Two of the key technologies to enable vehicle infrastructure interaction,advanced traffic management,and automated vehicles are automated roadway mapping and reliable vehicle state estimation.In this paper,we present an overview and new methods for the problems automated roadway mapping plus a discussion of the extension of these methods to the problem of vehicle state estimation.Results from the application of these methods to feature mapping and state estimation are presented.

GPS-based fine-scale mapping surveys for schistosomiasis assessment: a practical introduction and documentation of field implementation

Background Fine-scale mapping of schistosomiasis to guide micro-targeting of interventions will gain importance in elimination settings,where the heterogeneity of transmission is often pronounced.Novel mobile applications offer new opportunities for disease mapping.We provide a practical introduction and documentation of the strengths and shortcomings of GPS-based household identification and participant recruitment using tablet-based applications for fine-scale schistosomiasis mapping at sub-district level in a remote area in Pemba,Tanzania.Methods A community-based household survey for urogenital schistosomiasis assessment was conducted from November 2020 until February 2021 in 20 small administrative areas in Pemba.For the survey,1400 housing structures were prospectively and randomly selected from shapefile data.To identify pre-selected structures and collect survey-related data,field enumerators searched for the houses’geolocation using the mobile applications Open Data Kit(ODK)and MAPS.ME.The number of inhabited and uninhabited structures,the median distance between the pre-selected and recorded locations,and the dropout rates due to non-participation or non-submission of urine samples of sufficient volume for schistosomiasis testing was assessed.Results Among the 1400 randomly selected housing structures,1396(99.7%)were identified by the enumerators.The median distance between the pre-selected and recorded structures was 5.4 m.A total of 1098(78.7%)were residential houses.Among them,99(9.0%)were dropped due to continuous absence of residents and 40(3.6%)households refused to participate.In 797(83.1%)among the 959 participating households,all eligible household members or all but one provided a urine sample of sufficient volume.Conclusions The fine-scale mapping approach using a combination of ODK and an offline navigation application installed on tablet computers allows a very precise identification of housing structures.Dropouts due to non-residential housing structures,absence,non-participation and lack of urine need to be considered in survey designs.Our findings can guide the planning and implementation of future household-based mapping or longitudinal surveys and thus support micro-targeting and follow-up of interventions for schistosomiasis control and elimination in remote areas.