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Networked objects are learning to anticipate our needs and orchestrate responses that deliver safety, efficiency, and convenience.
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Jen: This anticipatory alarm received information from the cloud regarding weather and traffic, but also from the car itself. The car could also push a message to the cloud that its gas level was low. The system would then anticipate that the driver might have to stop for gas and add that to the expected commute time.
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Tim: The vast majority of trips that we take in vehicles tend to be trips we’ve made before, but having that information, creating a profile or history in order to derive conclusions about what someone might be doing on a Tuesday at 5:30, provides useful information. If it’s aggregated and people opt in, that can beneficially impact the traffic load balancing. It could help create an efficient use of infrastructure and help the overall impact of transportation as it plays out.
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Jennifer Healey, research scientist at Intel, and Tim Plowman, embedded user experience lead with Intel Labs’ Experience Design team.
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