[Decode CarTech] How Much Can Autonomous Cars Learn from Virtual Worlds? / How big data is transforming the automotive industry?

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How Much Can Autonomous Cars Learn from Virtual Worlds? By Evan Ackerman

To be able to drive safely and reliably, autonomous cars need to have a comprehensive understanding of what’s going on around them. They need to recognize other cars, trucks, motorcycles, bikes, humans, traffic lights, street signs, and everything else that may end up on or near a road. They also have to do this in all kinds of weather and lighting conditions, which is why most (if not all) companies developing autonomous cars are spending a ludicrous (but necessary) amount of time and resources collecting data in an attempt to gain experience with every possible situation.

In most cases, this technique depends on humans making annotations to enormous sets of data in order to train machine learning algorithms: hundreds or thousands of people looking at snapshots or videos taken by cars driving down streets, and drawing boxes around vehicles and road signs and labeling them, over and over. Researchers from the University of Michigan think there’s a better way: Doing the whole thing in simulation instead, and they’ve shown that it can actually be more effective than using real data annotated by humans.


How big data is transforming the automotive industry? by Gary Eastwood

The rapidly expanding Internet of Things (IoT) is seeing more and more devices connected to the internet. Traditionally, these have been biometric wearables, home appliances and audio-visual equipment. Automobile manufacturers, however, are making a play to corner this market for their own ends.

Entrenching Wi-Fi into automobiles opens an entirely new avenue of pursuit that entails vehicles communicating directly with the internet for GPS navigation, email and music streaming, for example.

By 2020, the connected car market report states that connected car services will account for approximately $40 billion annually. These services include infotainment, navigation, fleet management, remote diagnostics, automatic collision notification, enhanced safety, usage based insurance, traffic management and, lastly, autonomous driving. The root of these applications is big data, as increasing amounts of data are collected from remote sensors; this information is being interpreted and leveraged to transform the automotive industry into one of automation and self-sufficiency.

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