Navigant Research Blog

With Predictive Navigation, Smart Cars Find Their Own Way

— December 15, 2014

The flood of available data from many sources – traffic updates, GPS, onboard sensors, etc. – will change the ways in which we’ll get around in the coming years.  One tangible manifestation happening now is predictive navigation.

From Google to Bosch to Volkswagen, a range of companies in the automotive and technology industries are starting to harness the power data to provide personalized real-time guidance and enhanced vehicle control that could lead to reduced congestion and fuel consumption – and, eventually, to hybrid powertrains that automatically adjust the balance between battery and engine output based on upcoming terrain.

Go This Way

Data about where and when we travel and how fast we go is collected through a combination of built-in systems, such as General Motors’ OnStar and Hyundai’s BlueLink, and brought-in systems, specifically smartphone apps.  Every time a driver launches a navigation app, such as Waze, Google Maps, or TomTom, information about speed and location is transmitted back to the cloud and aggregated with other factors, such as weather forecasts, construction sites, and local events, to determine where backups are occurring or are likely to occur and to provide real-time feedback.   The macro data can be combined with local data about individual driver habits to automatically provide alerts about traffic backups and alternate routes before you turn the key.

Google has provided these predictive alerts for more than 2 years as part of the Google Now functionality on Android phones.  At a recent innovation workshop at its Wolfsburg, Germany headquarters, Volkswagen showed off its own in-car solution to provide alternative route suggestions even when drivers don’t need to use the navigation for common destinations.  Other automakers, including General Motors, have been testing solutions for plug-in electric vehicles, like the Chevrolet Volt, that will automatically preserve electric power for the last portion of a drive home through a residential area or even use up some of the low-charge buffer when the system predicts it will be plugged in soon.

Shortest Is Not Necessarily Most Efficient

Mercedes-Benz is now utilizing topographic map data as an input to the plug-in hybrid powertrain available in its S500 luxury sedan.  When the system detects that the vehicle is approaching the crest of hill, it will automatically shift the power distribution away from the internal combustion engine to the electric motor and then recover energy to the battery on the downhill side.   Ford has been researching eco-routing solutions for both plug-in and traditional vehicles that will calculate routes that use less total energy even though they may cover more total distance.

Everyone that drives in urban areas is well aware of the frustrations of sitting through several cycles of a traffic light while trying to make a left turn.  For the past decade, package delivery company UPS has been using big data and electronic maps to provide its drivers with customized daily routes specifically designed to keep left turns to a minimum.   By using right turns whenever possible, even if it means going further, UPS had saved more than 10 million gallons of gasoline and reduced carbon emissions by 100,000 metric tons by 2012.

 

Street Lights Add EV Charging

— December 11, 2014

Sometimes a solution forms at the intersection of two challenges that may not seem, at first glance, to have anything in common.  For example, cities are perpetually seeking ways to increase revenue, and many owners of electric vehicles (EVs) want access to ubiquitous charging infrastructure.

Enter the new concept of retrofitting street lights with money-saving LEDs and EV charging ports.  City managers are moving toward central control of street lights by adding a control node, which enables them to reduce cost and integrate the lights with other systems, as my colleague Jesse Foote recently wrote.  With smart street lighting technology (as covered in Navigant Research’s report, Smart Street Lighting) in place, EV charging capabilities can also be added to street lights, creating a new revenue stream for municipalities.

A Light and a Charge

Among the first pilots of this combination are occurring in the cities of Munich in Germany, Aix-en-Provence in France, and Brasov in Romania.  BMW has two such lights at its headquarters in Munich and will add a series of enhanced lights in the city next year.  A consortium called Telewatt, led by lighting manufacturer Citelum, is similarly installing LED street lights with EV charging in Aix-en-Provence.  In Romania, local company Flashnet has integrated its inteliLIGHT management platform with an EV charger.

Motorists can pay for the EV charging using a mobile phone app.  Cities that have regulations allowing them to provide EV charging services can gain revenue to help balance the books.  They can also balance the additional power demand of EVs within their overall power management system.  Placing a Level 1 or Level 2 charging outlet on a light pole reduces the installation cost of bringing power to the curb, which otherwise can be several times greater than the cost of the equipment.  Cities that install these systems will help drive demand for EVs, which has the added benefit of increasing urban air quality.

This is another example of the integration of seemingly disparate city services into a smart city.  As detailed by Navigant Research’s Smart Cities Research Service, the move toward integrating power, water, transportation, waste, and building management will yield considerable savings while improving the quality of urban life for city dwellers.

 

For Self-Driving Cars, Automakers Consult Silicon Valley

— December 10, 2014

The fully autonomous vehicle (AV) is coming, and early models will be on roads sometime around 2020.  To reach this milestone, automakers are turning to Silicon Valley for its expertise in connected devices, the Internet of Things, and human-machine interfaces.  A recent tour of the 18-month-old Nissan Research Center (NRC-SV) in Sunnyvale, California underscored the importance of this trend in relation to the automotive industry’s development of the AV of the future.

While some autonomous drive systems that rely on cameras, lasers, and sensors, such as lane keeping and automatic braking, don’t require vehicle connectivity, the fully autonomous vehicle will.

Reaction Time

The fact that AVs are likely to be far safer than non-autonomous, human-driven vehicles has been well-established.  However, to provide the type of near guaranteed safety the auto industry and customers require, the fully autonomous vehicle described by NRC-SV Director Martin Sierhuis will be “the most complex system in the world.”

For starters, the fully autonomous vehicle needs to be able to communicate with other vehicles and infrastructure, anticipate/predict human and non-human (animal) behavior, be personable, constantly observe and relay information back to the Internet, and act quickly upon information received from all these sources.

Watch for Deer

Information on weather conditions, traffic congestion, and road construction are valuable assets to other vehicles and, in an ideal system, can be transferred seamlessly.  Further, observations made by vehicles can be used to maintain a near real-time map of the world, given changes to road infrastructure.  However, the most valuable pieces of information will be on how AV predictive systems can be improved and how AVs fail.

A major challenge for AVs is the unpredictability of the world.   The awkward four-way stops, the sudden trajectory deviations, the deer on the side of the road, the ear buds-wearing bicyclists in downtown San Francisco, etc. all have to be accounted for.  To function effectively, an AV must be able to predict both human and animal behavior better than humans do.  Predictions are based on data; as more data is accumulated on humans through AVs, they will in turn be better able to predict human behavior and, therefore, safer in the more pedestrian-centric urban environments.

The above are all examples in which the sharing of information from AV to AV will avoid catastrophe; however, it must be assumed that failures will eventually happen.  Yet, the silver lining will be that when the AV eventually does fail, the circumstances of that failure will be shared, and the overall system will learn from it.  As Sierhuis explained, “The same accident will never occur again.”

 

Using Applications to Empower Smart Cities

— December 9, 2014

In late November, the crowdsourced smartphone app Waze released a stunning visualization that showed the traffic flowing through New York City on a recent September day.  Resembling blood flow though a body, cars move through the arteries and veins of city streets and highways, slowed by both collisions and general congestion.  Waze collects data via smartphone owners that allow their location (and speed and direction) to be captured and aggregated, providing real-time information on traffic in a city.  When using Waze, drivers can be alerted to new incidents like accidents and police on the road, and the app can even suggest new routes for a faster ride.

Crowdsourcing apps like Waze, ridesharing apps like Ridejoy, and home control apps like Nest may also be a boon to some of the smart city initiatives being developed and planned worldwide.  Numerous cities in the world are adapting IT for their infrastructure and streamlining operations for their departments.  Navigant Research’s report Navigant Research Leaderboard Report: Smart City Suppliers identifies the promising companies that have demonstrated advanced approaches and penetration in this sector.  Most smart city initiatives begin with public transportation and traffic monitoring, as they are critical services for citizens, and promote commerce as well.

Short on Cash

There’s a basic challenge for cities that want to pursue programs like these, though: limited municipal funding.  In the first world, or in a few examples in the developing world, cities have signed multimillion-dollar contracts, paying large IT and equipment companies for equipment and consulting services for smart city initiatives.  These large price tags limit the adoption of (large) smart city programs in the developing world and in smaller cities and towns.  Crowdsource apps could provide a solution.

If the data from crowdsourced apps like Waze could be shared with municipal agencies, data limitations would virtually disappear.  Instead of paying millions for a full service solution, a city could hire a cadre of data analysts to examine the trends in traffic, identify collision hot spots, and use the aggregated data for long-term traffic planning, supplanting expensive traffic studies.  One example of an interesting use of this kind of data is New York University’s (NYU’s) visualization of taxi rides in New York City.

Taxi Confidential

Using data from the Taxi and Limousine Commission, NYU researchers created a rich queryable database where taxi demand is revealed visually, and the impact of major disruptive events like Hurricanes Sandy and Irene on taxi rides can be understood (namely, that few taxis ventured into the power-less regions of lower Manhattan).  MIT has, in turn, developed an interactive website using the taxi data to demonstrate the value of ridesharing.   The academic insight has yet to be used for city policy, but as the analysis improves, such applications will surely follow.

Certainly, there are obstacles with this approach.  The first is privacy.  Aggregated urban mobility data can be anonymized.  Yet, the idea of governments gaining access to individual citizens’ whereabouts, regardless of the source of the data, may make a fair number of people uncomfortable.  Open questions prevail: Could mobility data be used for forensic purposes?  Since Waze is owned by Google, what other information could be associated and shared?  These questions and many others will have to be addressed through real deployment.  As has been seen through companies like Uber, which is now causing taxi medallion prices to fall, disruptive technologies can shake up the status quo.  City governments have not traditionally been the locus of innovation, but the smartphone in your pocket may change that in the near future.

 

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