China 
Africa 
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Introduction
Published in Yasmina Bestaoui Sebbane, Intelligent Autonomy of Uavs, 2018
Details of the certification process for agricultural aircraft operations are provided in FAA Advisory Circular 137–1A [45]. Using the hazard classification and analysis system (HCAS) taxonomy, hazards are identified from the causal narrative following the six-step aviation system risk model process. The U.S. National Agricultural Aviation Association (NAAA) has expressed concerns over the use of UAS for agricultural applications and has also identified a number of potential UAS hazards to the FAA [46]. The aviation system risk model precision agriculture notional scenario provides a systems-level framework for the integration of socio-technical hazards related to the UAS, the crop-duster, operations and the environment. Geo-fencing offers one mitigation strategy in the avoidance of mid-air collisions. A geo-fence, one component in the move towards UAS autonomy, uses the GPS, to check that a UAS is within its designated area of operation.
Error sensitivity of the connected vehicle approach to pavement performance evaluations
Published in International Journal of Pavement Engineering, 2018
Raj Bridgelall, Md Tahmidur Rahman, Jerome F. Daleiden, Denver Tolliver
The IRI achieves relatively high precision within few traversals because the procedure that produces the index uses a fixed quarter-car and a precise traversal speed. Conversely, connected vehicles rely on geo-fence triggering based on the geospatial position estimates from on-board GPS receivers. Therefore, variations in position tagging the inertial data stream lead to larger variations in the longitudinal traversal path analysed. Additionally, the roughness index derived from connected vehicle data reflects variations in the actual vehicle suspension behaviour, traversal speed and sensor parameters. The field studies conducted found that the connected vehicle approach could achieve the same level of precision as the IRI procedure within seven traversals. The results further indicate that for a given vehicle and segment lengths larger than 50 m, the MOE in estimating RIF-indices diminished below 1.5% as the traversal volume exceed 50.