AI News, #3 URBAN MOBILITY artificial intelligence
The WIRED Guide to Self-Driving Cars
In the past five years, autonomous driving has gone from “maybe possible” to “definitely possible” to “inevitable” to “how did anyone ever think this wasn’t inevitable?” to "now commercially available."
The details of the program—it's available only to a few hundred vetted riders, and human safety operators will remain behind the wheel—may be underwhelming but don't erase its significance.
Countless hungry startups have materialized to fill niches in a burgeoning ecosystem, focusing on laser sensors, compressing mapping data, setting up service centers, and more.
This cycle has restarted, and the term “driverless car” will soon seem as anachronistic as “horseless carriage.” We don’t know how cars that don’t need human chauffeurs will mold society, but we can be sure a similar gear shift is on the way.
Just over a decade ago, the idea of being chauffeured around by a string of zeros and ones was ludicrous to pretty much everybody who wasn’t at an abandoned Air Force base outside Los Angeles, watching a dozen driverless cars glide through real traffic.
So, Darpa figured, maybe someone else—someone outside the DOD’s standard roster of contractors, someone not tied to a list of detailed requirements but striving for a slightly crazy goal—could put it all together.
Each team grabbed some combination of the sensors and computers available at the time, wrote their own code, and welded their own hardware, looking for the right recipe that would take their vehicle across 142 miles of sand and dirt of the Mojave.
But the race created a community of people—geeks, dreamers, and lots of students not yet jaded by commercial enterprise—who believed the robot drivers people had been craving for nearly forever were possible, and who were suddenly driven to make them real.
Within 18 months, they had built a system that could handle some of California’s toughest roads (including the famously winding block of San Francisco’s Lombard Street) with minimal human involvement.
And the proliferation of ride-hailing services like Uber and Lyft weakened the link between being in a car and owning that car, helping set the stage for a day when actually driving that car falls away too.
The tech giants followed, as did an armada of startups: Hundreds of small companies are now rushing to offer improved radars, cameras, lidars, maps, data management systems, and more to the big fish.
The key tool for doing that perception work—seeing the difference between a stray shopping cart and a person using a wheelchair, for example—is machine learning, which requires not just serious artificial intelligence chops but also gobs upon gobs of real-world examples to train the system.
That’s why Ford invested a billion dollars into artificial intelligence outfit Argo AI, why General Motors bought a startup called Cruise, why Waymo has driven 10 million autonomous miles on public roads (and billions more in simulation).
In November 2018, Tesla debuted a feature called Navigate on Autopilot, which gives its cars (including those already on the road, thanks to an over-the-air software update) the ability to change lanes to get around slower drivers or to leave the highway when it reaches its exit.
At least two Tesla drivers in the US have died using the system (one hit a truck in 2016, another hit a highway barrier this year), and the National Transportation Safety Board has criticized Tesla for making a system that's too easy to abuse.
The huge automakers that build millions of cars a year rely on the complex, precise interaction of dozens or hundreds of companies, the folks who provide all the bits and bobs that go into a car, and the services to keep them running.
Instead, expect to see these robocars either debut as highway-bound trucks or in taxi-like fleets, operating in limited conditions and areas, so their operators can avoid particularly tricky intersections and make sure everything is mapped in excruciating detail.
You know how fiercely Uber and Lyft fight for market share today, tracking drivers, trying to undercut each other, and piling up promotions to bring in riders?
It’s easy to conjure up a dystopia, a world where robocars encourage sprawl, everyone lives 100 miles from their job, and sends their self-driving servants to do their errands and clog our streets.
The optimists imagine a new kind of utopian city, where this technology not only eliminates crashes but integrates with existing public transit and remains affordable for all users.
MOBiLus, a consortium of 48 partners from 15 countries has won the EIT Urban Mobility - Mobility for Liveable Urban Spaces.
Under Horizon2020, the EIT’s current annual funding to its Innovation Communities gradually increases to reachover EUR 80 million after few years of activity - provided that they achieve the expected results.
- On 30. november 2020
Future of mobility
We unveil the findings of our major global study into mobility. Singapore and several European capitals lead the way but there is much work to be done ...
Audi - Future Urban Mobility
Subscribe Now: Συνδεθείτε τώρα με την Audi: Βρείτε τον
What We Need To Know About A.I. - Dr. Roman V. Yampolskiy/Alex Klokus - WGS 2018
What We Need to Know About A.I.? Dr. Roman V. Yampolskiy, Professor of Computer Science, University of Louisville, answers this question. #WorldGovSummit ...
CogX 2018 - Will an AI be your next Chief Marketing Officer?
Wes Nichols, Board Partner, Upfront Ventures ---------------------------------------------------------------------------------------------------------------- CognitionX: The AI Advice ...
Open the Blackbox: Autonomous Intelligent Driving GmbH
Wie bekommen wir ein Auto dazu, sich ohne Fahrer sicher durch die Stadt zu bewegen? Annika spricht dazu mit Dennis Heine, einem Strategen bei der ...
MFA Urban Quest: Anthony Elliott – The Future of Cities
Anthony Elliott, Research Professori of Sociology in the University of South Australia, talks about the Future Citites. What will our cities look like, as artificial ...
Transnet: Understanding traffic with open data and visualization
Abstract: Contemporary urban policy makers face a critical challenge over the next decade or so: transport infrastructure needs to be able to cope with growing ...
Building a financial system that can deliver the future we want
10 years on from the global financial crisis, we brought together a group of the leading figures in sustainable finance to ask: what's next? What will it take to ...
The Future of Artificial Intelligence w/ Dr. Ayesha Khanna @ayeshakhanna1 #DataTalk
Every week, we chat with data science leaders from around the world on Facebook Live. This data science video series is part of Experian's effort to help people ...
Visual Feature Based Localization