for many of the roads today—that covers 70% of developed society—but we
need to go much further and faster.
You don’t want to miss centimeters in
a self-driving car.
“At first, people believed a navigable
map was unaffordable; now, they think
HD mapping is unaffordable. It’s not
unaffordable; you have to be clever
about it,” De Taeye says.
Through a deal with nVidia, Tom-
Tom aims to accelerate HD map cre-
ation by applying more advanced ar-
tificial intelligence (AI) algorithms
running on graphics processors in the
vehicles themselves, as well as in the
cloud. Huang says nVidia’s approach
employs AI to work out the difference
between trees, buildings, and other
vehicles. “We detect all these for two
reasons. Number one: we want to con-
tinuously update our map, and there
are several different types of marker
we can use to figure out where we are.
Number two: we’re detecting where it’s
safe to drive [in real time].”
With the front end of the map-
ping technology deployed in vehicles,
TomTom and others want to gather
mapping data from many vehicles to
support a continuously updated map.
“Crowdsourcing will be helpful. Other-
wise, it would take many thousands of
one’s own vehicles, like Google’s, and
many miles of driving, resulting in
high cost, to achieve these HD maps—
and they would not be up to date, ei-
ther,” says Kevin Mak, senior analyst
for Strategy Analytics’ global automo-
tive practice.
Marco Lisi, engineering manager
for global navigation systems at the
European Space Agency (ESA), points
to handheld gadgets as rich sources
of mapping data. “Whenever we carry
around a smartphone, we are carrying
around several sensors: a compass,
accelerometer, gyroscope, and GPS.
We are collecting data on our location
all the time with several sensors at
once,” Lisi says.
Such real-time data streams already
feed back into location-driven applications such as the StreetBump app used
by the City of Boston, which collects
data on potholes from users—the app
forwards data from the motion sensors in smartphone handsets when the
vehicle they are riding in hits a bump.
The Waze app uses regular updates on
the location of smartphones carried by
its clients as they drive around in their
cars; data from the app not only high-
lights traffic jams, but lets the host
servers estimate the number of lanes
on a freeway based on the geographic
spread of pings across the road col-
lected over time.
Eric Gunderson, CEO of MapBox,
sees similar data aggregated on a large
scale being used to perform precision
mapmaking. “What we’re getting every
single day is data that represents 100
million miles of traveling. As it comes
in, it just looks like noise, but as I ana-
lyze the data, the algorithm can discern
the actual lanes on the highway. You can
drill down this crowdsourced data all
the way to put center lines on the road
so I can drive the car as if it was on rails.”
A lack of standards for represent-
ing sensor data even for dedicated ve-
hicle systems will make crowdsourcing
more difficult in the short term, Mutz
says. “Nowadays, most vehicles have
different sensor setups and there are
no established standards for the stor-
age and distribution of that data.”
Although mapping organizations
are likely to embrace standards to al-
low them to incorporate data from
many sources, a fully open ecosystem
seems unlikely, says Strategy Analyt-
ics’ Mak, who says the companies
involved will prefer to maintain semi-
closed ecosystems.
Some of the standards used to exchange mapping data will be mandated by government: agencies such as the
U.S. National Highway Transportation
Safety Administration (NHTSA) see
the potential for crowdsourced data to
improve traffic safety, as well as map
accuracy. A car can send messages to
nearby vehicles if it detects a pedestrian moving toward the road, or passes a
vehicle signaling that it intends to turn
across oncoming traffic.
Working with the U.S. Department
of Transportation, the NHTSA said in
2015 it was speeding up plans to man-
date the adoption of vehicle-to-vehicle
(V2V) communication. Based on a ver-
sion of the Wi-Fi local-area network
protocol adapted to work in a dedicat-
ed frequency band around 5.9GHz to
limit interference, V2V allows cars to
share data on the environment around
them. If a car detects a pedestrian mov-
ing toward the road or passes a vehicle
signaling that it intends to turn across
oncoming traffic, it can send messages
to the vehicles following behind.
The communication need not be
limited to vehicles. Says Maurice Ger-aets, senior director of chipmaker
NXP Semiconductors, “There will be
cameras at intersections that send
V2X signals.” Such smart intersections
will be able to indicate whether nearby
cars need to slow down for a red signal
or warn that a car has blocked an exit.
Temporary roadside beacons will alert
vehicles to the presence of roadside
workers, and that can be added to local
maps temporarily.
“Collaboration across many technologies is very important,” says Dean.
Yet the vehicles and their mapping
software will need to be alert to the possibility of hacking, and of collaborators
in data being less than honest. Lars Reger, chief technology officer of NXP’s
automotive division, says without effective security, “I could put a little beacon in front of my house and transmit
to the world that an accident has happened, and clear the road outside.”
Further Reading
Seif, H.G, and Hu, X.
Autonomous Driving in the iCity – HD Maps
as a Key Challenge of the Automotive
Industry, Engineering: The Official Journal
of the Chinese Academy of Engineering and
Higher Education Press, Vol. 2, Issue 2,
June 2015, pp159-162
Carrera, F., Guerin, S., and Thorp, J.
By the People, for the People: the
Crowdsourcing of ‘StreetBump,’
an Automatic Pothole Mapping
App, International Archives of the
Photogrammetry, Remote Sensing and
Spatial Information Sciences (ISPRS),
Volume XL-4/W1, 29th Urban Data
Management Symposium (2013)
Harding, J., Powell, G., R., Yoon, R., Fikentscher,
J., Doyle, C., Sade, D., Lukuc, M., Simons, J., and
Wang, J.
Vehicle-to-vehicle communications:
Readiness of V2V technology for
application, National Highway Traffic Safety
Administration Report No. DO T HS 812 014.
Mutz, F., Veronese, L.P., Oliveira-Santos, T., de
Aguiar, E., Auat Cheein, F.A., and De Souza, A.F.
Large-Scale Mapping in Complex Field
Scenarios Using an Autonomous Car, Expert
Systems with Applications. Vol. 46, 15
March 2016, pp439-462
Chris Edwards is a Surrey, U.K.-based writer who reports
on electronics, IT, and synthetic biology.
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