Inside Unmanned Systems provides actionable business intelligence to decision-makers and influencers operating within the global UAS community. Features include analysis of key technologies, policy/regulatory developments and new product design.
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63 unmanned systems inside April/May 2016 ENGINEERING. PRACTICE. POLICY. still made by the driver. Yet, the system signifi- cantly increases the driver's situational aware- ness, which is instrumental in reducing the ac- cident rate. Indeed, recent studies indicate that the advisory-mode cooperative safety principle would prevent 81% of accidents. Examples of such accidents are illustrated in Figure 8. For these examples, the V2V cooperative technology completely "sees" the oncoming traf- fic based on the inter-vehicle exchange of navi- gation data. The system predicts a possibility of collision and recommends a corresponding prevention action: engaging brakes for the first example and merging back to the lane for the second example, respectively. Currently, one of the key challenges for such advanced V2V systems is the lack of navigation solutions that satisfy V2V accuracy requirements for all driving scenarios (i.e. open-sky, tree-cov- ered roads, benign and dense urban) and meet the cost limitations of the automotive market. An automotive navigation module (ANM) that supports accurate navigation capabilities in real time is required to enable reliable prediction and prevention of traffic accidents. The currently an- ticipated accuracy requirement is for horizontal positioning in the 1.5-meter range at 95% con- fidence level. GNSS technology can satisfy this requirement in open-sky areas. However, in many signal-challenged environments, GNSS performance degrades rapidly and cannot sup- port lane-level accuracy for reliable prediction and avoidance of automotive accidents. To address this limitation, our PnP sensor fusion solution has been applied to low-cost sensors suitable for automotive market. The application specifically focuses on the use of sensors that are already installed in cars for other purposes such as odometer and video- camera. As better sensors become available and/or the sensor mix shifts due to technologi- cal advancements, the plug and play approach is able to rapidly incorporate these advancements to lower unit cost and improve performance. In addition, the same software package can be Figure 11: Portable data collection system: the system is designed to mount in a standard 19" rack. Figure 12. Example test environments; Google Earth and Street View mode were used to obtain the photographs. utilized for different vehicle models (from basic to luxury) from different automakers to opti- mally accommodate available sensors. Finally, the solution can be used for development and testing purposes to rapidly assess the influence of specific sensors on the localization accuracy (e.g., when new sensors become available). Experimental Demonstration For experimental demonstrations of PnP navi- gation capabilities, a ground vehicle test setup