Motor vehicles are becoming ever more intelligent. The goal of this development is to drive comfort & to improve traffic safety. cape to cape challenge.

1. Business white paper
Big data
analytics for the
car of the future
Cape to Cape Challenge reveals potentials

2. 2
Motor vehicles are becoming ever more intelligent –
catchword: “connected car.” The goal of this development is
not only more driving comfort, however, most importantly, it
is an improvement in traffic safety and traffic control.
Comprehensive datasets were collected last fall as a part of
the record test drive – “Cape to Cape Challenge 2014”. Their
analysis indicates the direction that should be taken in the
synthesis of vehicle and IT. The application cases range from
new insurance models and optimized fleet management to
more efficient road maintenance; what is referred to as the
“smart city.”
– By Jürgen Dettling and Christoph Kielmann*
Modern passenger cars and trucks are small data centers on wheels. Computers analyze
ongoing vehicle performance in order to improve operation and to support the driver.
On-board electronics, for example, issue a warning if the tire pressure deviates from the
target value; the navigation system independently changes the route if a traffic jam would
greatly delay the trip; the communication system automatically imports new contacts
from the driver’s smartphone so that they can be accessed at the touch of a button on the
steering wheel; the Fatigue Detection suggests a rest period in the case of deviations from
normal driving behavior; and, finally, assistance systems relieve the driver of more and
more bothersome tasks. These include avoiding oversteer and understeer on wet roads,
staying in the lane on the freeway as well as automatic parking on the roadside or in the
parking garage.
The networked vehicle is on the way
Many of these functions looked like science fiction twenty years ago, yet, more and more of
them will become possible in the next ten or twenty years – it will even become standard, at
least, in the higher-value vehicles. Consider all the developments regarding the “connected
car” (the vehicle that is always connected to the Internet) or the “Internet of Things” (where
intelligent machines and devices synchronize themselves without requiring intervention by
the user).
In order to gain a further insight into how such a connected car can be best utilized for
intelligent traffic concepts, the IT provider HP and the automotive engineering specialist
IAV supported the famous record chaser Rainer Zietlow’s1
“Cape to Cape Challenge 2014.”
HP contributed its experience with modern IT systems, cloud services and the quick analysis
of comprehensive, complex data (big data analytics); IAV brought expertise with on-board
electronics and vehicle sensor technology.
*
Jürgen Dettling is Chief Technologist for HP in
Germany, Christoph Kielmann is Department
Director of the specialty department Chassis
Development for IAV GmbH.
1
challenge4.de
Business white paper | Big data analytics
for the car of the future

3. 3
Rainer Zietlow’s team was unable to adhere to the set timeframe to reach South Africa’s,
Cape Aghulas in the record time of nine days, with a driving distance of 19,000 km from the
North Cape in Norway, due to a traffic accident in Africa caused by a third party. Nevertheless,
his VW Touareg still delivered an abundance of enlightening sensor data collected during the
drive – not to mention from the accident. In combination with information such as weather
data etc, this provided useful insights for the further development of intelligent networked
vehicles – as well as for associated social media communication.
Analysis of vehicle performance
Rainer Zietlow’s VW Touareg recorded 70 driving-style signals during the Cape to Cape
Challenge: How did he accelerate, break, and enter a curve? How was steering corrected in
the curve? When and how often did the driving assistance systems have to intervene? When
and how often was cruise control used? The record-attempting test drive delivered a total of
1.6 billion database rows that HP had to analyze via the Vertica data bank - Image files from
the dashboard camera and additional input such as traffic information, weather data and
social media reactions were also analyzed by HP.
Rainer Zietlow’s VW Touareg delivered much enlightening measured data from the Cape to Cape
Challenge 2014.
During the trip, Zietlow’s Touareg was connected to the Helion Cloud, where the big data analytics of the
varied input took place.
Cape to Cape Challenge 2014
At the end of September, Rainer Zietlow
and his two co-drivers started their
journey to complete the 19,000 km from
the North Cape,Norway to Cape Agulhas,
the southernmost point in Africa, in only
nine days as a part of the Cape to Cape
Challenge 20142
. Zietlow used a VW
Touareg V6 TDI Clean Diesel with 224 HP
(165 kW). This vehicle had a standard motor,
however, also included a modified chassis,
roll-over cage and extensive special
on-board IT equipment. In his attempt
to set a record the car was installed with
diverse additional sensors, which delivered
information about vehicle performance
and road quality, a front camera regularly
took pictures of the road conditions, and
navigation software ensured orientation.
The general public were able to follow the
trip, live, via an app that was developed
by HP, specifically, for the Cape to Cape
Challenge. In addition to regular posts on
Facebook, Twitter and Instagram, Zietlow’s
Team also made daily publications of
current updates and images, in order for
the fans to keep up-to-date.3
2
touareg-capetocapechallenge.com
3
See facebook.com/rainer.zietlow.
challenge4
twitter.com/capetocape2014
instagram.com/rainer.zietlow
Business white paper | Big data analytics
for the car of the future

4. 4
The measured values for driving performance were forwarded to a HP specialist in the USA
for big data analytics. Based on the measured data, he was able to determine that three
different drivers operated the car during the Challenge – which was accurate: Rainer Zietlow
was on the road with two co-drivers and they all took turns driving.
Such results can be determined by correlating the vast amount of information in a multi-
dimensional model according to frequency and/or intensity over time, in order to recognize
striking cluster formations in the structured as well as unstructured datasets. One indicator,
for example, was the analysis of the use of cruise control. It revealed that one of the three
drivers frequently used the cruise control with speeds of 50-60 km/h, the second one
predominantly at 150-160 km/h, the third one, in turn, preferred 120-130 km/h.
The big data analytics proved: Yes, it is possible to reveal different driving styles through
the analysis of vehicle data. Such insights can be utilized for a variety of applications. A car
insurance provider, a car rental agency or the manager of a fleet of company cars could
reward especially careful driving in the future - and thereby differentiate between several
users of the same vehicle. Car rental agencies, for example, could offer their customers more
economical rates based on an individual analysis of trip data (“Pay As You Drive”), insurance
companies could grant special discounts and fleet owners could link bonus programs to
desired driving behavior, verified by diverse indicators such as the use of automatic distance
in truck convoys.
Analysis of road conditions
In addition, the possibility of measuring and evaluating road conditions during the drive of
the Cape to Cape Challenge 2014 was also considered. To accomplish this, the on-board
and additional sensors on Zietlow’s Touareg collected information on road-related driving
behavior such as: How strong is the vertical acceleration? How big are the differences
between the left and right wheel?
The analysis displays the different driving behavior of the three Touareg drivers in three distinct clusters.
Sentiment analysis in social media
Aside form the analysis of vehicle-
related data, the Cape to Cape Challenge,
also, delivered valuable information in
a completely different context: Since
Zietlow’s team allowed the fans to
participate in the extreme ride via posts
on Facebook, Twitter and Instagram; the
analysis in the HP Helion Cloud was able to
gather interesting insights regarding the
social media environment of the trip. HP,
for instance, developed a special app for
the Cape to Cape Challenge that provided
users with up-to-date information on the
record trip and encouraged interaction. The
analysis revealed that HP received the most
positive response from the social media
audience in comparison to all the other
sponsors of the trip.
Throughout the trip, HP consistently
monitored social media comments and
forwarded positive reactions to the app.
The sentiment analysis of the big data
platform resulted in only 4.7% negative
posts, that had to be filtered out. The
highest level of positive as well as negative
comments were related to the accident in
Africa; negative reactions were, primarily,
due to the lack of news on progress, until
the affected users realized that there had
been an accident; positive involvement, on
the other hand, was mostly encouraging
the drivers in order to motivate them in
light of the setback.
An unexpected revelation: The involvement
of social media users reached its peak level
16 hours after the accident – and this was
not due to different time zones. Via big
data analytics of empirical data, the Cape
to Cape Challenge provided information
on the most effective time for a company
to become involved on the social media
platform after a significant event.
Business white paper | Big data analytics
for the car of the future

5. 5
The Touareg generated thousands of measurement data per second - values far beyond
what is customary today. The chassis sensors transmit 1000/s per sensor (4000/s in all),
other sensors with 100/s and yet others with 10/s. Sensing with millisecond precision is
common within the control devices (e.g. for the electronic stability program). Such data had
only been previously encountered in the testing environment and not in series-production
vehicles. That would have quickly overloaded the CAN bus.
It was a big challenge for the on-board IT equipment to transmit this enormous amount
of real-time data to the cloud for analysis – especially since there was no stable Internet
connection, depending on the area. In light of the extreme conditions, the vehicle repeatedly
generated more data than the uplink to the cloud could handle. Therefore the Cape to Cape
Challenge also indicated the fact that there is still a need for development, for the future
generation of connected cars.
Despite these obstacles, the big data analytics of the Challenge resulted in four data
clusters. There was an automatic subdivision into four road types:
• Smooth road
• Gravel road
• Pot holes
• Gravel road with pot holes
The information that was collected on road conditions – the infamous “Road to Hell” in Kenya
proved to be the worst stretch of road4
– this information could be transferred to a map and
color-coded via the GPS data supplied by the vehicle. The aggregation of such sensor data
thereby identified which roads in a specific area were in most need of repairs. Correlation of
this with the frequency of road usage will result in an empirically verifiable priority list for the
responsible authorities.
The standard as well as additional sensors turned the challenge vehicle into a measurement vehicle in
terms of road quality.
4
See also touareg-capetocape.com/video/
Business white paper | Big data analytics
for the car of the future

6. 6
Even though the Touareg was equipped with additional sensor technology for such
measurements, in this case they were commercially available vehicle sensors. Such sensors
are already installed in diverse premium vehicle classes today – in the Touareg as well as in
the VW Phaeton, for example. This creates interesting future scenarios. Luxury class models,
for example, can serve as “road quality scouts” for the other cars of a manufacturer and
notify other drivers, for instance, of frost damage on the road surface in a timely manner.
The navigation system or control display of less expensive cars could then issue warnings
such as: “Caution – road with pot holes!” – long before warnings signs call attention to
these dangers.
Swarm intelligence and smart city
Even though today’s navigation systems are capable of changing the planned route on short
notice based on information of traffic congestion, they are not yet capable of delivering
current data for increased safety or greater driving comfort. Questions regarding the
connected car become interesting when the focus is not merely on the individual vehicle,
but when the real-time communication of networked vehicles including dynamic interaction
in the manner of the “Internet of Things” is considered. This could enable future vehicle
generations to automatically organize themselves via the constant exchange of traffic
information, so that optimum traffic flow and a high degree of traffic safety are guaranteed
via swarm intelligence.
If the interaction between intelligent vehicles truly occurs in real-time at some point, then
even scenarios are conceivable with a vehicle involved in an accident that automatically
warns the cars that are following it: “Caution, accident around the next curve!” – While
vehicles even farther back are automatically rerouted to a bypass.
This swarm intelligence can also be used on a community level – catchword: “smart city” –
to optimize many aspects of route guidance. This ranges from efficiency monitoring of
synchronized traffic lights to the question: where, when and how does road traffic need to
be regulated in order to make a frequently used route to school from point A to B as safe
as possible?
The measured data of the Cape to Cape Challenge shed light on where the road conditions were
particularly critical.
Business white paper | Big data analytics
for the car of the future

7. From the Challenge to real-time traffic optimization
Scenarios such as the smart city, or connected cars using swarm intelligence to help each
other reach their destinations faster, place enormously high demands on the interaction
of IT and automobile. The Cape to Cape Challenge 2014 indicated how difficult it can
be to transmit extensive amounts of data to the cloud in real-time under poor radio
communication conditions. The plan to obtain even more sensor data from vehicles in the
future, analyze it in the cloud, aggregate it as needed and then transmit it back to the very
vehicles that need this information, is all the more complex. Concept studies such as those
by HP and IAV as a part of the Cape to Cape Challenge 2014 provide important information
for the future optimization of traffic flow as well as traffic safety.
© Copyright 2015 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. The only
warranties for HP products and services are set forth in the express warranty statements accompanying such products and services. Nothing herein
should be construed as constituting an additional warranty. HP shall not be liable for technical or editorial errors or omissions contained herein.
February 2015
The interaction of networked vehicles via swarm intelligence in a smart city can help optimize traffic flow.
Business white paper | Big data analytics
for the car of the future