Cars already generate more data that you might imagine. Telematic diagnostic devices (such as GM’s OnStar) have been in use for two decades and funnel gigabytes of data back to the manufacturer. Other types of devices such as insurance-related monitoring equipment, toll transponders and navigation equipment generate data about almost every aspect of driving.
This is only the beginning, as Autonomous Vehicles (AVs) will generate significantly more data than conventional cars. This data is essential for AVs, as they use it to train the algorithms that driverless vehicles rely on, and for machine learning to speed up the process of improvement.
Companies such as Dash Labs, Automatic and Otonomo offer platforms that utilise this data. It has become increasingly apparent that there is huge commercial potential in harvesting it, as vehicle-generated data is extremely valuable to all kinds of businesses, from parts manufacturers and fleet managers to companies which sell location-based services.
One of the most interesting developments in this part of the AV sector is the fact that two models have emerged – and they mirror the classic Android vs IoS rivalry.
On the one hand, Uber, Tesla and Google are all racing flat-out to develop a proprietary driverless car. Generating, harvesting and utilising their own data is a huge part of this effort.
And on the other hand, there open-source efforts, the biggest of which is led by Apollo, the AV project being led by Chinese tech giant Baidu. This project has brought together over 50 companies in order to try to open-source driverless technology. Those involved include Microsoft, TomTom, Nvidia and Bosch – an indication of how important the vehicle data market is predicted to be over the coming years.
It is too early to say which approach will win the race to mass-market an AV – but it is clear that data is a huge part of race preparation.
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