In Analytics, Internet of Things, Use Cases

You become more adept at driving familiar routes over time. You learn the best times to leave to avoid traffic, which highway lanes move faster, and where large puddles form after heavy rainstorms. Could your connected car also become more adept over time?

Using the spatial, temporal and network analysis of situational intelligence, a connected car could learn your normal commuting routes and times, correlate those with weather records and your fuel efficiency performance, and offer suggestions for how to drive the route more fuel efficiently. For connected hybrid cars, such data could be transformed into optimizing when to draw on battery charge versus gasoline for more fuel efficiency. Similar analytics might make alternate suggestions if you prefer to make the trip as quickly as safety allows, regardless of fuel efficiency.

Connected cars could perform their own version of air conditioning demand response. On hot days, cars could analyze the forecasted outside temperature along your route, or draw on outside temperature readings from other connected cars ahead of you. Those other cars could also share their operating performance related to specific stretches of road. Your car would then combine this with your interior comfort preferences and your current fuel supply and usage.

Based on all this information, your car could control your air conditioner in much the same way your utility controls air conditioning during demand response event, pre-cooling the cabin on flat stretches of road then throttling back cooling while driving up hills. This would mean better fuel efficiency for you without sacrificing comfort while you drive.

Demand response is just one way that connected cars could learn from one another to increase operational efficiency. Crowd-sourced predictive maintenance might be another way.

Suppose that past data shared between connected cars shows that drivers in your city with your make and model of car tend to need their water pump replaced after 57,000 miles of in-city driving, regardless of highway driving miles. Based on this data, your car could warn you of a potential pump failure before it happens.

Your car could even, with your permission, share this water pump information with others. Your mechanic might receive the update and order the part ahead of your next appointment, saving you time at the shop and preventing a future breakdown while out on the road. The car manufacturer could use this water pump information to improve product design and reliability and inform dealers of potential issues. Makers of after-market car parts might pay to receive this sort of reliability information to help them design and market products.

Connection, analytics and machine learning are still on the horizon for the average car, but that horizon is fast approaching. I think we’ll find great new conveniences and operating efficiencies as that horizon draws near.

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