Communications of the ACM

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