AI News, nuTonomy to Test World's First Fully Autonomous Taxi Service in Singapore This Year

nuTonomy to Test World's First Fully Autonomous Taxi Service in Singapore This Year

As car companies large and small make steady but incremental progress towards the commercialization of autonomy in consumer vehicles, the big question is when we're going to finally see (and be able to benefit from) full, level 4 autonomy.

So far, we can buy cars that come equipped with autonomous braking, autonomous parking, and autonomous highway driving, but fully urban autonomy has only been demonstrated by a few, and not in a form that's ready for consumers to take advantage of.

Singapore and MIT have been collaborating on research projects like these since 2007, and nuTonomy is one of the results of this partnership: part of nuTonomy's 25-member core team comes directly from the team that developed those autonomous golf carts.

As companies like Google have demonstrated, we have the (very expensive) hardware that's necessary for autonomous urban vehicles, but the software that tells those vehicles what to do based on the data their sensors collect is still a work in progress.

A Level 4 autonomous vehicle 'is designed to perform all safety-critical driving functions and monitor roadway conditions for an entire trip;' all you have to do is provide a destination and (possibly) open and shut the doors.

It's a big step from Level 3 to Level 4, but the benefits are enormous: in addition to leaving the driving completely to the car, it also means that the car is capable of driving itself with no human inside, which is what makes a robotic taxi service possible.

'The reason for that is the technology in this area isn't primarily automotive technology—it's really being drawn from the robotics community, technology that's been developed in robotics research labs over the last 20 years.

The problem with incremental progression towards autonomy in personal vehicles, Iagnemma explains, is fundamentally one of cost: 'you're not trying to sell a feature to a customer, who might only be willing to pay a couple thousand dollars, which really constrains your sensor and computer cost.'

Removing consumer ownership from the equation with a commercial vehicle, like a robotic taxi, completely changes things, however: 'Now you're trading against the cost of a human driver, so you have a lot fewer constraints on your cost,' Iagnemma says.

mobility-on-demand system only really makes commercial sense in urban areas, and urban areas are the most challenging for autonomous vehicles because of the density and complexity of information that needs to be understood in order to make safe and productive decisions.

So one of the really unique and differentiating things that we're doing is building into our decision-making engine the ability for cars to actually violate the rules of the road when it's necessary to do so, it in a safe and reliable manner.

And then we use algorithmic processes to translate these rules into logical structures that are verifiable, meaning that we're sure that the structures that come out of these rules exactly represent and adhere to the rules that we define.

This verifiability is a huge benefit, because when you take an alternative approach, which is to just manually hand-engineer a ruleset, it's very difficult to convince yourself that that ruleset exactly represents the rules you'd ideally like the car to follow, especially when the ruleset is large and the situations are complex.

Even with its unique and sophisticated software, it's somewhat surprising that a company as young (and small) as nuTonomy could very well be the first company in the world to deploy a true Level 4 autonomous vehicle in commercial operation in an urban area.

They have a progressive approach towards appropriate legislation around autonomous vehicles, and then working with technology providers, car manufacturers, and other groups to insure that they'll be able to operate in a reasonable way.'

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MIT 6.S094: Introduction to Deep Learning and Self-Driving Cars

This is lecture 1 of course 6.S094: Deep Learning for Self-Driving Cars taught in Winter 2017. Course website: Lecture 1 slides: Contact: