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[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.

[Decode CarTech] What happens when self-driving happens

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What happens when self-driving happens by Nabeel Hyatt

A couple weeks ago I spoke to a working group of economists and climate change experts in Norway about what’s happening right now in autonomous driving: how fast it’s likely to go mainstream, the effects it might have, and the opportunities this creates both in government and in business. Below are my remarks.

Imagine what it must have felt like to be in a major urban center like New York or Boston in the early 1900s when, in a sea of horses and humans, the first automobile “put putted” past. Now imagine, if you will, three people standing nearby, with three different reactions to that event: The first, “Wow, I want to get one of those!” the second, “Wow, I want to build my own car company and make one too.” and lastly, “That thing is going to need a place to get gas.”

I believe fully autonomous driving has the potential to have as large an effect on our way of living as the car itself did. So it’s worth getting past the immediate “Wow” reactions, as well as some of the initial knee-jerk fears, and start to lay the groundwork for what’s coming next.


Seeing into the future

The image of internet-based electric BMW models autonomously picking up customers using online mobility services may seem outlandish at this moment, but will become commonplace in the next 10 years.

It is thanks to BMW Group’s innovation leadership in mobility and autonomous driving in recent years.

“BMW wants customers to experience premium mobility in both an individualized and emotional way,” said Harald Kruger, chairman of the board of management of BMW AG.

“Our goal is sustainable mobility. With our services, we are available whenever and wherever the customer needs us.”

The services are based on use of intelligent devices, including the BMW Mobility Mirror and a cloud platform that decides the best route through a virtual interface.

“BMW aims to be a mobility solutions provider for more than 100 million customers in 2025 and all areas of future mobility will be integrated as demonstrated in its centenary celebration last year in the Vision Next 100 series vehicles,” he said.


Cadillac has a secret weapon in its quest to beat Tesla at self-driving

In order for Cadillac to feel confident enough to introduce the industry’s first truly hands-free driving system to the public, the car company wanted to be sure it had enough data on the US highway system before it launched. How much data? Well, all of it.

To do this, Cadillac didn’t deploy a fleet of camera-mounted vehicles to record footage of the nation’s highways, like Google does for Street View. Nor did it rely on “fleet learning” like Tesla, in which many vehicles operating on the same software work together to build a more detailed map. Instead, Cadillac used vehicles equipped with high-powered LIDAR sensors to build a highly detailed map of the US highway system.

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