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Big data analytics for the car of the futureCape to Cape Challenge reveals potentials

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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

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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 2014At 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.com3 See facebook.com/rainer.zietlow.

challenge4twitter.com/capetocape2014instagram.com/rainer.zietlow

Business white paper | Big data analytics for the car of the future

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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 mediaAside 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

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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

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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

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