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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42 unmanned systems inside Spring 2014 ground cepts might be technically possible, the existing system presents a formidable challenge to mak- ing this vision a reality. The current traffic pattern in the United States for example, consists of 250 million human-driven vehicles, whose operators don't always behave rationally and legally, but in- stead often behave erratically. Unexpected situa- tions on the road, such as pedestrians, animals, fallen objects, or detours and maintenance, make it difficult to pre-program the vehicle path and pre-determine vehicle behavior for all scenarios. Imagine situations where other vehicles don't come to a complete halt at a stop sign, a ball rolls on the road, or lane markings are missing or changed. These situations make it clear that humans, with their experience, instinct, and pattern recognition, have skills essential for navigating today's traffic that are difficult—if not impossible—for a computer to acquire. So, while conceivably not impossible, releasing the human from all driving tasks might take much longer than current demonstrations suggest. What Consumers Think Automated vehicles present a paradigm shift in the way we drive, and even if the technology is there, consumers will need to buy into the con- cept. Although many people may be willing to spend thousands of dollars for the conveniences these vehicles provide, safety concerns may keep them from adopting the new paradigm. Opinion surveys, for instance, suggest that consumers would be willing to spend several thousand dollars on systems that release them from the burden of navigating the vehicle through stop-and-go traffic or controlling it constantly on a long-distance journey. However, they may still find it difficult to trust a driverless vehicle com- pletely, continuing to worry about potential sys- tem failures, data privacy and cyber security. Learning by Example Automation has, however, become a reality in re- cent years for ground vehicles other than passen- ger vehicles, which creates a learning opportunity. For instance, large-scale dump trucks in the min- ing industry haul ore out of surface pits without a human behind the wheel, and only remote op- erators can stop the vehicle in an emergency. Con- tainer carriers are operated in harbors in a similar way, while agricultural tractors collect crops from the (human controlled) harvester and discharge at the processing plant. Another interesting example of early-stage driverless vehicles is automated mobility-on- demand (AMOD) systems in pedestrian areas, amusement parks, or at airports. These vehicles, sometimes dubbed as "horizontal elevators" or "people movers," operate on a fixed route. Pas- sengers can call them to any predefined stop (e.g. "parking lot") for a transfer to any other stop (e.g. "department store"). What do all these concepts have in common? The deployment area is either closed (i.e., no unauthorized humans or vehicles are present) or limited (i.e., the operating area is precisely determined and pre-mapped), and often a communication infrastructure is established so vehicles can be halted in an emergency. Because of these conditions, and also be- cause of the relatively low speed of the respec- tive vehicles, such scenarios do not directly ap- ply to the self-driving passenger vehicle vision. Nonetheless, with such examples, industry and government can develop and test safety mea- sures that can move us further toward a wider adoption of automated vehicles. Stanley, an autonomous car developed by the Stanford University Racing Team, won the 2005 DARPA Grand Challenge after successfully traversing a 132-mile course. " " Computers don't get distracted, sleepy, or incapacitated like people do. Photo courtesy of the Smithsonian Air & Space Museum