Power of Prediction: Developing Context-Aware Cars

The connected car revolution is gearing up, transforming a conveyance that gets you from point A to point B into a powerful personalized device that keeps you seamlessly connected from any location and at any time and predicts your wants and needs along the way.

An altogether new type of car will soon hit our streets: Cars that connect with consumers’ digital lives. Cars that get better and better the more they are driven. Cars that become increasingly intelligent as they learn and adapt to the users’ personality over time. These cars will recommend likely destinations, maneuvers or routes based on the user’s previous actions and current situation. Like a good butler, they will anticipate the driver’s needs, including making recommendations for places to visit along the route.

Car manufacturers have become good at gathering and storing data. Now it’s all about leveraging this wealth of data and making use of it. To develop personalized, contextually relevant applications and meet next generation customer expectations, OEMs have to put data into the overall context of driving and making it intelligent. Clearly, data and intelligence are not the same thing. Automakers need to work out algorithms that reduce noise as they define and extract truly valuable information.

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Data gathered by the Connected Car has limited value if not combined with context – Tweet That!

Raw data is not actionable or useful, but it does become useful by adding context. In order to accurately summarize and represent the data that has been collected, layers of driver-dependent context, or personalization, should be applied. These in turn feeds the user experiences that drivers see and feel when driving the vehicle. Contextualization and personalization is the key to transforming data into real information and delivering personalized context-aware experiences and superior user experiences. With a contextual understanding of the driver, the car can offer a huge choice of applications that anticipate the driver’s wants and needs and thus make the overall experience of driving the vehicle more interesting, safer and more compelling. To master contextualization, demographic, historical and contextual data has to be analyzed:

  • Contextual data refers not only to geo-location but also to road conditions caused by traffic, weather, time of day/week/year or other factors
  • Historical data will contain a person’s visited places, prior routes and actions.

CloudMade is devoted to bringing contextual relevance to drivers. Our products are designed to give both drivers and OEMs actionable insights, thus creating a better experience for drivers and generating significant business insights for OEMs. CloudMade understands the context of the driver, using machine learning against a multitude of content sources like traffic, current and predicted weather, news, events, POIs and more. Cars with CloudMade’s technology are smarter and feel particularly personal to their driver.

Learn more about CloudMade’s context awareness engine: Predictive Learning System

November 12th, 2014 - Posted by in for OEMs |