UMTRI team investigating “instantaneous crowdsourcing” to speed adoption of autonomous vehicles

Combining human and artificial intelligence in autonomous
vehicles could push driverless cars more quickly toward wide-scale
adoption,
according
to researchers at the University of Michigan
Transportation Research Institute (UMTRI).

That’s the goal of a new project that relies on a technique
called instantaneous crowdsourcing to provide a cost-effective,
real-time remote backup for onboard autonomous systems without the
need for a human to be physically in the driver’s seat.

Today’s autonomous vehicles can drive relatively well in
typical settings, but they fail in exceptional situations—and
it’s those situations that are the most dangerous. Designing
autonomous systems that can handle those exceptional situations
could take decades, and in the meantime, we’re going to need
something to fill the gap.

—Walter Lasecki, an assistant professor of computer science
and engineering and a leader of the project

The need for human safety drivers in vehicles such as Waymo’s
recently introduced autonomous taxis undermines their cost
advantage compared to traditional ride sharing services, the
researchers say. It also keeps the era of cars as autonomous
rolling living rooms out of reach. Most researchers agree that
machines won’t be able to completely take over driving duties for
years or even decades.

Instantaneous crowdsourcing differs from earlier efforts at
remote human backup in that it can provide human responses in just
a few milliseconds—potentially fast enough to help dodge a
swerving vehicle or maneuver around a piece of roadway debris. It
utilizes connected vehicle technology and a remotely located
control center.

Here’s how it would work—all within the space of five
seconds or less:

  1. Software in the vehicle would analyze real-time vehicle data and
    electronically guesses 10-30 seconds into the future to estimate
    the likelihood of a “disengagement”—a situation where the
    car’s automated systems could need human help.

  2. If the likelihood exceeds a pre-set threshold, the system
    contacts a remotely located control center and sends data from the
    car.

  3. The control center’s system analyzes the car’s data,
    generates several possible scenarios and shows them to several
    human supervisors, who are situated in driving simulators.

  4. The humans respond to the simulations and their responses are
    sent back to the vehicle.

  5. The vehicle now has a library of human-generated responses that
    it can choose from instantaneously, based on information from
    on-board sensors.

Such a system might sound expensive and cumbersome, but Robert
Hampshire, a research professor at UMTRI and U-M’s Ford School of
Public Policy, says it would be far less expensive than having a
human driver in every vehicle. This could make it particularly
valuable to ride-sharing and fleet operators. And the huge volume
of miles driven combined with the fact that autonomous vehicles
only rarely need human assistance could drive economies of scale
that would bring down the cost per vehicle.

There were 3.2 trillion miles driven in the US last year, and
the best autonomous vehicles averaged one disengagement every 5,000
miles. We estimate that if all those miles were automated, you’d
need around 50,000 to 100,000 employees, distributed city by city.
A network like that could operate as a subscription service, or it
could be a government entity, similar to today’s air traffic
control system.

—Robert Hampshire

The algorithm-based screening at the beginning of the process
makes it more useful than earlier attempts at remote human
assistance, which required the vehicle to stop, contact a remote
call center and get instructions before proceeding.

Another key to making the system work on the ground will be
designing it in a way that’s workable for the large number of
employees, says Hampshire.

Like the job of air traffic controllers, this work could be
stressful and cognitively complex. So we’ll be looking at ways to
make it less intense and mentally fatiguing.

—Robert Hampshire

The developers are currently working on the software platform.
They hope to have humans testing the system by the end of the
project’s first year, with the system capturing data from actual
vehicles by the end of the second year.

The basic premise behind instantaneous crowdsourcing was
validated in a paper titled “Bolt:
Instantaneous Crowdsourcing via Just-in-Time Training
,” which
was presented at the ACM CHI 2018 conference.

We introduce the look-ahead approach, a hybrid intelligence
workflow that enables instantaneous crowdsourcing systems (i.e.,
those that can return crowd responses within mere milliseconds).
The look-ahead approach works by exploring possible future states
that may be encountered within a short time horizon (e.g., a few
seconds into the future) and prefetching crowd worker responses to
these states. … Through a series of crowd worker experiments, we
demonstrate that the look-ahead approach can outperform the fastest
individual worker by approximately two orders of magnitude. Our
work opens new avenues for hybrid intelligence systems that are as
smart as people, but also far faster than humanly possible.

—Lundgard et al.

The USDOT project aims to adapt it for use in autonomous
vehicles. In addition to USDOT, this project is funded by the
Center for Connected and Automated Transportation at UMTRI, Mcity
and U-M’s Mcubed.

Resources

Source: FS – Transport 2
UMTRI team investigating “instantaneous crowdsourcing” to speed adoption of autonomous vehicles



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