Vehicle-to-Cloud – yes, it is a thing!  And it is making automotive insurance providers and telecoms thrive together to support telematics

“Self-driving cars are the natural extension of active safety and obviously something we should do,” this is a popular quote by Elon Musk. But wait, even more popularly discussed are  the data privacy challenges which come with connected cars. The solution to these challenges rest in the world of Telematics, a method of monitoring an asset (car, truck, heavy equipment, or even ship) by using GPS and onboard diagnostics to record movements on a computerized map.

Telematics might be a decade old phenomenon, but what is relatively new to telematics, and innovating at a high speed, is the effort to connect telematics to the internet of things (IOT); and find out how telematics solution providers can benefit from cloud-based management services. The critical question is how the data generated within a vehicle will effectively interact with data of the outside world through connected devices. We all are waiting for an era where vehicles will not only drive themselves but also talk intelligently to us. This makes the role of telematics service providers important who offer services to vehicle drivers for either a subscription fee or any other arrangement. These can be emergency services or information services to improve the driving experience.

To keep up with the recent government regulations across the globe and cope with climate change, fleet operators are strongly emphasising energy saving and vehicle safety measures. At the same time, they want to ensure business models which can reduce Total Cost of Operations (TCO), which encompasses acquisition cost, operational cost and depreciation. This is where connectivity-as-a-service can help telematic service providers.

Here is how it works: Data is gathered from vehicles and transmitted to a cloud-based platform and then used for various services depending on the functionality of the app-  location, fuel consumption or speed. Apps interact with telematics systems to see how fleet companies are operating. (see chart below)

The numbers speak of the potential of this growing industry for automotive, cloud-based solutions and telecommunications, among other third-party providers. According to  Global Telematics Market Report released by Netscribes, the global telematics market is expected to grow at a compound annual growth rate (CAGR) of 28.5% between 2017 and 2022 to reach a global revenue of USD 233.24 billion by 2022. Here are a few areas where connectivity-as-a-service can help telematic service providers.

Compliance with Government regulations

Governments in several countries have made it mandatory to have an electronic onboard recorder (EOBR) fitted commercial vehicle, which is one of the key drivers of the adoption of telematics among fleet companies. Today, North America is the highest revenue-generating region for the automotive telematics industry. The telematics market is at a mature stage in countries like the US and Canada, where stable growth is forecasted until 2022. The ELD mandate, passed this year, limits how much truck drivers can drive and when; for example, they cannot drive for more than 11 hours during a 14-hour period. These rules have required telematics service providers to keep a track record of necessary data to ensure driver safety and compliance.  

Documentation of real-time data by fleet operators

The primary role of telematics service providers is to measure driving data to offer services like driver behaviour analysis, safety training integration, predictive analysis and connected vehicle frameworks. To provide any of these solutions, one needs to accurately collect real-time data generated by vehicles from various devices and then transform it into a structured form to make accurate business decisions. For instance, helping an insurance company know the driver performance data to offer better products. With the documentation of real-time data, solution providers can access cloud-based management systems through mobile or desktop.

Security and safely in vehicles

Statista predicts that by 2020, connected cars will make up 98% of the new car market worldwide. With this innovation also comes challenges regarding security. Sample this: In 2016, a team from security research firm Keen Labs successfully demonstrated how to hack Tesla’s Model-S. They tricked drivers into accessing a fake website through a malicious Wifi hotspot, which then downloaded the researchers’ software. This software enabled them to gain control of the car’s features. The solution to protect connected cars also lies in connectivity-as-a service, which will enable transparency and decentralisation of data.

Customisation for various business models through collaboration

Cloud platforms allow drivers, carriers, shippers, fleet operators, dealers, service stations, insurance companies and other authorities to be connected in real-time with each other. The likes of Google and Alexa can get information on the local road safety and what steps to advise next in terms of recommendations to users. Through connected platforms, service providers can give personalised advice gathered on the basis of contextual data such as geolocation, traffic zone etc. It is as simple as Alexa helping a user to make the next decision with the help of a connected device through different apps. What route to follow? Which restaurant to visit? What speed to maintain?

Conclusion

It is clear that the IOT  is revolutionising fleet management. The newest generation of Fleet Management Solutions have migrated to the IOT. The clear benefits of an effective vehicle-to-cloud platform are increased driver safety, reduced TCO and predictive analysis for the future.

To deliver optimum results to telematics solution providers and fleet operators, there is a need for reliable connectivity across coverage areas, and compatibility with networks– which a reliable connected platform can provide.

We are leaving you with a list of ten telematics service providers:

The advent of a sharing economy has brought a sea of change to the way we commute in the city. The Lyfts of the world have made taxi riding convenient, affordable and safe. These rides have emerged as a strong alternative to public transport, clocking millions of rides per month in some cities. The emergence of hyper-local delivery models to optimize the supply chain has also led to a large number of daily trips by these vehicles.

These developments have brought about the ubiquity of either standalone or smartphone app-based GPS devices to keep track of and better regulate these rides and  fleets of taxis. These GPS systems spew a ton of data generating up to GBs of data per second. With automobile & technology experts predicting that self-driving cars would replace human-driven cars in no more than a decade, the volume and velocity of GPS data is only set to increase. With that context in mind, it becomes imperative to understand GPS data and the kind of insights which can be obtained by analyzing it.

A GPS or a GPS-enabled device can produce all or some of the data points mentioned below at a specified frequency (generally one record per second):

• Coordinates – The latitude and longitude values are the primary data points provided by GPS devices. A set of latitude and longitude values is sufficient to locate a point on the earth. For example, (51.5007° N, 0.1246° W) denotes Big Ben in London. Just to brush up, latitude is the angular separation of a point from the equatorial plane in north or south direction while longitude is the angular separation of a plane containing the point in east or west direction relative to the plane containing the prime meridian. A collection of latitude and longitude values over time can reveal the trail left by the vehicle.
• Direction – This data point denotes the geographic direction in which the vehicle is moving at that instant. A direction of 450 would mean that the vehicle is headed in north-west direction while 2250 would mean that is going in south-west direction. North is taken as the reference (00)
• Speed – The instantaneous rate at which the vehicle is travelling.
• Timestamp – A timestamp data point can be stripped to get year, month, day, hour, minute and second information from each record
• Additional data – GPS enabled devices can also send additional information like whether a taxi is carrying a passenger or not or the amount of payload a truck is carrying. These become very powerful when combined with coordinates and timestamp data.

Since the size of GPS data is usually huge, it makes sense to load such data into distributed file frameworks like HDFS and then process it using tools like Hive and Spark. The processed results can be visualized in tools like R Shiny, Tableau, D3.js and Excel. If the data size is small and if one is interested in prototyping an analytics use case then Python can be used as well.

With such rich data at our disposal, a variety of analytics use cases can be performed depending upon the business context. The most common of them are as follows:

1) Distance between two points – The coordinates of two points can be used to calculate the radial distance between them. Most frequently, a central point of a city is chosen as the base and the distance of the vehicle from this base is calculated at different instants of time. The distance is calculated using a Haversine formula given by following expressions. Assume there are two points P1(lat1, long1) and P2(lat2, long2). The radius of the earth is R. Then

dlat=lat1-lat2

dlong=long1-long2

a=(sin⁡(dlat2))2+cos (lat1) *cos (lat2) *(sin⁡(dlong2))2

c=2*arcsin⁡(a  )

distance=R*c

                      dlat and dlong should be converted to radians before calculating a.

The implementation of this calculation in Python can be done as shown below:

2) Dividing a an area into square grids – If a city or town can be divided into multiple grids of a specified equal size and insights are obtained for these individual grids, it becomes much easier to implement those insights. Here is an abridged recipe for how this can be achieved (a detailed one would require a blog of its own):

1. Decide a center for the city along with the number and the size of the grids wanted. Suppose you want 900 1kmX1km grids. You would need a square of side 30km.
2. Find the line of constant longitude at a distance of 15km from the chosen center on either side (left and right) of the center. Similarly, find the line of constant latitude at a distance of 15km on top and bottom sides from the center. These lines would give the edges and their intersection would give the vertices of the overall square
3. Find the latitudinal and longitudinal span of the edges and divide the span into 30 equal parts. Call them latd and longd. Start from one edge to reach the other edge by incrementally increasing the latitude and longitude by these values.
4. Draw lines of constant longitude and latitude at those points. This would result in 30 vertical and 30 horizontal lines and their intersection would produce 900 grids with all their vertices with known latitude and longitude

These grids can be visualized using leaflet library in D3.js or R Shiny.

3) Temporal averages of important metrics – The timestamp data can be used to gauge trends about the additional data across various timeframes. For example, daily averages of distances covered in each hour. These time frames can be nested as well to get a more granular picture e.g. a plot of average payload for each half hour of the day for each day of the week. The relevant time element needs to be gleaned out of timestamp followed by a grouping of the relevant metric column by the time element. An indicative temporal visualization would look as the one shown below. The horizontal axis shows the day of the week while the vertical axis shows the half hour of the day while the metric has been shown as the heat map gradient.

Geospatial analytics can unravel many mysteries and can help organizations optimize routing to match supply and demand, fight theft and related frauds and minimize the chances of accidents or the damage caused by it.

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Last Friday saw AT&T presenting new prototypes and discussing the future of technology at their Innovation Showcase in New York. Among the prototypes displayed were GPS sensors for luggage, software for streamlining equipment management in businesses and accessible data visualisation software than can be run from a laptop or tablet.

Indeed, an overarching theme of the event seemed to accessibility and interconnectivity. “We used to be more protective,’ explained Marion Croak, AT&T’s senior vice president of Applications and Services Infrastructure. “Now, we make APIs available … and give developers tools to use so that reliability and resiliency—things that can take years to develop—are available to any innovator [immediately] and is theirs to use”.

Greater openness and accessibility will allows users “to have access to [a new kind of experience],” she continued. “Customers will be given the tools to create their own services. It will be your network. You’ll be able to design it.”

Proof-on-concepts on display at the innovation showcase included:

• Smart Luggage- A GPS sensor built into luggage that allows users to track their luggage, and even receive text alerts when their luggage reaches the airport. It also has a flashing LED light to allow you easily identify the suitcase at the luggage carousel. The current model has hit a few stumbling blocks: the GPS allows you to see broadly where your suitcase is, but not specifically where it is in an airport; the battery only last three days; and FAA regulations concerning GPS & wireless connectibility mean it’s not possible to use the tracker during transatlantic flights. But, Smart Luggage is still very much a work in progress; AT&T are looking to resolve some of these issues, as well as working on integration with RFID and Beacons.
• Nanocubes- Nanocubes is AT&T’s big data visualisation project. What makes it different from all of the other visualisation technologies swamping the market is that it doesn’t rely on vast amounts of local resources to run; it can be accessed from a laptop or tablet. Nanocubes is AT&T’s answer to making big data visualisation broadly accessible.
• Project Halo- Project Halo is designed to co-ordinate and streamline logistics around equipment maintenance. The current demo is centered around kitchens in Disney resorts, in partnership with Disney Sync Link. In short, if a dishwasher broke down, Project Halo would send out an alert to all workmen in the area who could fix it. The worker could then accept the assignment, find out the appliance’s exact location, view repair manuals and submit a work order- all through Project Halo.

“When you look at the historic events in telecom,” Christopher Rice, vice president of Advanced Technologies and Architecture at AT&T. “There’s the move from analog to digital, then from voice to data—and then cellular. We’re in one of those exciting moments again now, moving to software.” From the technologies presented, it certainly seems that AT&T is moving smoothly into this new era of technology.

Read more here.

(Image credit: Jeepers Media)