[LINK] Self-Driving Cars Have a Bicycle Problem

[Bernard Robertson-Dunn]

<brd>
Maybe they should ban all those trouble-making pedestrians and cyclists.
Problem solved.
</brd>

Self-Driving Cars Have a Bicycle Problem
Bikes are hard to spot and hard to predict
By Peter Fairley Posted 24 Feb 2017 | 21:00 GMT
IEEE Spectrum
http://spectrum.ieee.org/transportation/self-driving/selfdriving-cars-have-a-bicycle-problem

Robotic cars are great at monitoring other cars, and they’re getting
better at noticing pedestrians, squirrels, and birds. The main
challenge, though, is posed by the lightest, quietest, swerviest
vehicles on the road.

“Bicycles are probably the most difficult detection problem that
autonomous vehicle systems face,” says UC Berkeley research engineer
Steven Shladover.

Nuno Vasconcelos, a visual computing expert at the University of
California, San Diego, says bikes pose a complex detection problem
because they are relatively small, fast and heterogenous. “A car is
basically a big block of stuff. A bicycle has much less mass and also
there can be more variation in appearance — there are more shapes and
colors and people hang stuff on them.”

That’s why the detection rate for cars has outstripped that for bicycles
in recent years. Most of the improvement has come from techniques
whereby systems train themselves by studying thousands of images in
which known objects are labeled. One reason for this is that most of the
training has concentrated on images featuring cars, with far fewer bikes.

Consider the Deep3DBox algorithm presented recently by researchers at
George Mason University and stealth-mode robotic taxi developer Zoox,
based in Menlo Park, Calif. On an industry-recognized benchmark test,
which challenges vision systems with 2D road images, Deep3DBox
identifies 89 percent of cars. Sub-70-percent car-spotting scores
prevailed just a few years ago.

Deep3DBox further excels at a tougher task: predicting which way
vehicles are facing and inferring a 3D box around each object spotted on
a 2D image. “Deep learning is typically used for just detecting pixel
patterns. We figured out an effective way to use the same techniques to
estimate geometrical quantities,” explains Deep3DBox contributor Jana
Košecká, a computer scientist at George Mason University in Fairfax,
Virginia.

However, when it comes to spotting and orienting bikes and bicyclists,
performance drops significantly. Deep3DBox is among the best, yet it
spots only 74 percent of bikes in the benchmarking test. And though it
can orient over 88 percent of the cars in the test images, it scores
just 59 percent for the bikes.

Košecká says commercial systems are delivering better results as
developers gather massive proprietary datasets of road images with which
to train their systems. And she says most demonstration vehicles augment
their visual processing with laser-scanning (ie lidar) imagery and radar
sensing, which help recognize bikes and their relative position even if
they can’t help determine their orientation.

Further strides, meanwhile, are coming via high-definition maps such as
Israel-based Mobileye’s Road Experience Management system. These maps
offer computer vision algorithms a head start in identifying bikes,
which stand out as anomalies from pre-recorded street views. Ford Motor
says “highly detailed 3D maps” are at the core of the 70 self-driving
test cars that it plans to have driving on roads this year.

Put all of these elements together, and one can observe some pretty
impressive results, such as the bike spotting demonstrated last year by
Google’s vehicles. Waymo, Google’s autonomous vehicle spinoff, unveiled
proprietary sensor technology with further upgraded bike-recognition
capabilities at this month’s Detroit Auto Show.

Vasconcelos doubts that today’s sensing and automation technology is
good enough to replace human drivers, but he believes they can already
help human drivers avoid accidents. Automated cyclist detection is
seeing its first commercial applications in automated emergency braking
systems (AEB) for conventional vehicles, which are expanding to respond
to pedestrians and cyclists in addition to cars.

Volvo began offering the first cyclist-aware AEB in 2013, crunching
camera and radar data to predict potential collisions; it is rolling out
similar tech for European buses this year. More automakers are expected
to follow suit as European auto safety regulators begin scoring AEB
systems for cyclist detection next year.

That said, AEB systems still suffer from a severe limitation that points
to the next grand challenge that AV developers are struggling with:
predicting where moving objects will go. Squeezing more value from
cyclist-AEB systems will be an especially tall order, says Olaf Op den
Camp, a senior consultant at the Dutch Organization for Applied
Scientific Research (TNO). Op den Camp, who led the design of Europe's
cyclist-AEB benchmarking test, says that it’s because cyclists movements
are especially hard to predict.

Košecká agrees: “Bicycles are much less predictable than cars because
it’s easier for them to make sudden turns or jump out of nowhere.”

That means it may be a while before cyclists escape the threat of human
error, which contributes to 94 percent of traffic fatalities, according
to U.S. regulators. “Everybody who bikes is excited about the promise of
eliminating that,” says Brian Wiedenmeier, executive director of the San
Francisco Bicycle Coalition. But he says it is right to wait for
automation technology to mature.

In December, Wiedenmeier warned that self-driving taxis deployed by Uber
Technologies were violating California driving rules designed to protect
cyclists from cars and trucks crossing designated bike lanes. He
applauded when California officials pulled the vehicles’ registrations,
citing the ridesharing firm's refusal to secure state permits for them.
(Uber is still testing its self-driving cars in Arizona and Pittsburgh,
and it recently got permission to put some back on San Francisco streets
strictly as mapping machines, provided that human drivers are at the wheel.)

Wiedenmeier says Uber's “rush to market” is the wrong way to go. As he
puts it: “Like any new technology this needs to be tested very carefully.”



-- 

Regards
brd

Bernard Robertson-Dunn
Sydney Australia
email: brd at iimetro.com.au
web:   www.drbrd.com
web:   www.problemsfirst.com
Blog:  www.problemsfirst.com/blog