Since the 1980s Formula One pit crews have been using telemetry data transmitted from probes connected to the engine, chassis, brakes and driver to optimize vehicle setup, driver performance and race strategy. It is an essential contributor to winning. Similarly, telemetry for road vehicles has always been valuable. Development vehicles are heavily instrumented to help dial in settings for suspension, engine management and a host of other systems. For over twenty years automotive OEMs have been aggregating and analyzing telemetry data every time your car is hooked up to a diagnostic computer at a franchised dealer. It helps trace faults, identifies and applies needed software updates, and ultimately leads to better built, longer lasting vehicles.
With the advent of cheap reliable cellular communications in the 1990s connected vehicles went mainstream. GM launched OnStar in 1996 and moved to 4G/LTE embedded radios in 2014, closely followed by other OEMs. New vehicles offer rich infotainment experiences and can provide constant telemetry to be aggregated analyzed and acted on (1ms latency and up to 10Gbps with 5G).
This has not gone unnoticed by the leading technology companies, all of which extended their ecosystems to smartphones over the past decade adding tens of billions to their market capitalizations in the process. So far two have tipped their hand (a little): Apple released CarPlay in 2014 and Google followed with Android Auto in 2015. They both extend the phone experience familiar to over a billion users to the vehicle, displaying a simplified interface on the infotainment screen. This is just the beginning: at Google’s recent IO conference Android Auto was demonstrated driving a 17″ center display and a full LCD instrument cluster. This required integration with powertrain management, climate control, and other vehicle systems historically walled off by oems. The real prize is not device or system sales, it is to collect as much useful data as possible with the lowest latency and to monetize it across three categories:
Modern vehicles have over 100 compute nodes and hundreds of probes sitting on multiple networks. Aggregating and analyzing this data can identify when component is nearing failure so that a replacement can be waiting at the dealer when you bring your car in. It could eliminate OEM emissions certification by reporting the vehicle’s actual environmental impact (interesting to tax authorities). Aggregating data across OEMs or suppliers would enable a quantitative ranking of attributes that Consumer Reports and industry analysts can only approximate. Every interaction between a vehicle and its occupants generates data that can be used to tune the Human Machine Interface (HMI) in much that same way that firms like Facebook and Amazon are continually testing refinements to their user interfaces.
New cars know a lot about their immediate surroundings: external temperature and precipitation, rear and increasingly 360° video, speed and traffic density (from park assist, adaptive cruise control and lane change assist sensors), road conditions (from accelerometers and abs). Aggregating this data with low latency provides lane level insight into what is happening on the roads. Diversions, debris, animals (live, dead and in-between), potholes, accidents, oil patches can all be identified and drivers alerted–easing congestion and improving safety.
Machine learning from a massive number of data points will provide accurate predictive traffic models of tremendous use to roadway planners in the long term, and as inputs to traffic signal phase and timing in the short term. Aggregating navigation destinations from a critical mass of vehicles, in conjunction with the predictive traffic model, will enable routing based on very accurate lane level traffic forecasts and provide drivers with estimated journey time at different departure times.
Determining accident fault becomes easier when you have telemetry from the crashed vehicles, adjacent vehicles and video streams of the incident from multiple angles.
Every time you interact with a Google, Facebook, Amazon or other sophisticated cloud-based service you are revealing something about your preferences, interests and behavior–enriching their profile of you. This enables you to receive better service, quickly find things that are important to you and avoid much of the chaff. It also enables more granular marketing, creating higher conversion and ad rates. Studies have demonstrated that smartphone users willingly sacrifice privacy if offered something of value in return; vehicle occupant data is no different. Using a combination of vehicle, smartphone and key fob data it is trivial to identify vehicle occupants with a very high degree of certainty making privacy and anonymity notional without taking extreme measures.
You may not be able to see the drool, but this is causing much salivating in Mountain View. This is a winner takes pretty much everything game: think Google (>70% market share) vs Bing (<15%), Android (>80%) vs Apple (<15%), Facebook (1.1B active users) vs Google Plus+ (120M), you get the idea. If you are player number four or five it is probably best to pack up and go home. Traditionally, the automotive industry does not work this way. For example, Porsche has about 0.25% global market share yet generates similar profits to OEMs with more than an order of magnitude more sales. The difference is that winning technology platforms create a vitreous cycle that builds and reinforces both the user base and ecosystem. Users are invested in their ecosystem and want their favorite apps. Developers go where the users are: don’t hold your breath for that Android/iOS app to appear on Windows Phone. The best developers are unlikely to build applications for OEM platforms regardless of how elegant the API is if most of the users are on CarPlay and Android Auto, it just does not make economic sense. Without broad app support and a critical mass of users you don’t have a valuable ecosystem and you are not going to win.