Who (or what) drives a
driverless car?
It's more complicated than you think.
This article was written by Hussein Dia from Swinburne
University of Technology, and originally published by The Conversation.
A legal opinion by
the US National Highway Traffic Safety Administration (NHTSA) set the internet
alight in February. The US road safety federal regulator
informed Google that the artificial intelligence (AI) software it uses to
control its self-driving cars could effectively be viewed as the
"driver" for some (but not all) regulatory purposes.
The NHTSA‘s letter was in response to a
request from Google seeking the NHTSA’s interpretations of the US Federal Motor Vehicle Safety
Standards.
It was widely viewed in the media as a
recognition from the Feds that Google’s AI software, the self-driving system
(SDS), is legally the same
as a human driver. The details of the letter, however, tell a very
different story.
First, the letter strictly stated the term
"could be" equivalent to a human driver, meaning this definition is
yet to be settled.
The NHTSA’s letter also suggested that
suitable tests would need to be developed to allow the NHTSA to certify the SDS
compliance with road safety legislation.
And therein lies the challenge. What procedure
can be used to verify compliance? Should the AI self-driving software pass a
benchmark test, developed specifically for autonomous vehicles, before it can
be recognised as a legal driver? Who should develop such a test and what should
it include?
Driving the future
Make no mistake, car manufacturers and
technology companies are working towards a vision of fully autonomous
vehicles, and that vision includes taking the human driver out of
the loop. They have already made huge advancements in this space.
The self-driving software that has been
developed, based on 'deep neural
networks', includes millions of virtual neurons that mimic the
brain. The on-board
computers have impressive supercomputing power packed inside
hardware the size of a lunchbox.
The neural nets do not include any explicit
programming to detect objects in the world. Rather, they are trained to
recognise and classify objects using millions of images and examples from data
sets representing real-world driving situations.
But the driving task is much more complex than
object detection, and detection is not the same as understanding. For example,
if a human is driving down a suburban street and sees a soccer ball roll out in
front of the car, the driver would probably stop immediately since a child
might be close behind.
Even with advanced AI, would a self-driving
vehicle know how to react? What about those situations where an accident is
unavoidable? Should the car minimise the loss of life, even if it means
sacrificing the occupants, or should it protect the occupants at all costs?
Should it be given the choice to select between these extremes?
These are not routine instances. Therefore,
lacking a large set of examples, they would be relatively resistant to deep
learning training. How can such situations be included in a benchmark test?
Turing tests
The question of whether a machine could
'think' has been an active area of research since the 1950s, when Alan Turing
first proposed his eponymous test.
The basis of the Turing Test is
that a human interrogator is asked to distinguish which of two chat-room
participants is a computer, and which is a real human. If the interrogator
cannot distinguish computer from human, then the computer is considered to have
passed the test.
The Turing Test has many limitations and
is now considered obsolete.
But a group of researchers have come up with a similar test based
on machine vision, which is more suited to today’s AI evaluations.
The researchers have proposed a framework for
a Visual Turing
Test, in which computers would answer increasingly complex questions
about a scene.
The test calls
for human test-designers to develop a list of certain attributes that a picture
might have. Images would first be hand-scored by humans on given criteria, and
a computer vision system would then be shown the same picture, without the
'answers', to determine if it were able to pick out what the humans had
spotted.
There are a few vision
benchmark data sets used today to test the performance of
neural nets in terms of detection and classification accuracy.
The KITTI data set, for
example, has been extensively used as a benchmark for self-driving object
detection. Baidu,
the dominant search engine company in China, which is also a leader in
self-driving software, is reported to have achieved the best detection score of 90 percent on
this data set.
At the Consumer
Electronics Show earlier this year, NVIDIA demonstrated
the performance of its self-driving software on new data sets from Daimler and
Audi.
The demonstrations showed advanced levels for single and
multi-class detection and segmentation, in which the software was
able to extract more information from video images.
A modified Visual Turing Test can potentially
be used to test the self-driving software if it’s tailored to the multi-sensor
inputs available to the car’s computer, and is made relevant to the challenges
of driving.
But putting together such a test would not be
easy. This is further complicated by the ethical questions surrounding
self-driving cars. There are also challenges in managing the interface between
driver and computer when an acceptable response requires broader knowledge of
the world.
Policy remains the last major hurdle to
putting driverless cars on the road. Whether the final benchmark bears any
resemblance to a Turing-like test, or something else we have not yet imagined,
remains to be seen.
As with other fast-moving innovations,
policymakers and regulators are struggling to keep pace. Regulators need to
engage the public and create a testing and legal framework to verify
compliance. They also need to ensure that it is flexible but robust.
Without this, a human will always need to be
in the driver’s seat and fully autonomous vehicles would go nowhere fast.