Ramprasad Renganathan's picture

Jun 22, 2017


Ramprasad Renganathan

Data Modeler

Eighty-seven years ago a self-taught radio engineer named Bill Lear and a colleague, Howard Gates, created a prototype radio that would work inside an automobile. In doing so, they triggered not just an automotive advance but a whole new and still immensely popular “thing” in American motoring and, indeed, in American culture: cruising down the highway, windows down, radio blaring.

Just as Lear and his business partners married two technologies that had developed alongside one another — the automobile and the radio — it’s about to happen again. Today, two technologies that have been developing in parallel in recent years — voice assistants and big data analytics — are about to be combined into a potentially business-changing, culture-changing, and maybe even life-changing “thing.”

And I expect it to happen soon in the cabs of America’s big commercial trucks.

Over the last decade, the trucking industry has been rapidly increasing its use of onboard technologies that not only provide drivers helpful traffic warnings and directions but also monitor engine and rig safety and performance parameters, load management data, and even drivers’ hours and performance indicators. Such systems are being hailed as game-changers in terms of improving truck safety and adding much-needed efficiency improvements to the nation’s critical logistics chains.

Meanwhile, leading consumer technology services providers like Apple, Amazon, and Google, working with various tech hardware makers, have brought voice-activated services and devices like Siri, the Amazon Echo with Alexa, the Google Home device, Cortana (the voice-activated Windows 10 interface) and, later this year, Apple’s much anticipated HomePod. That technology, which when it debuted in the iPhone just six years ago was still buggy, is so advanced that it can distinguish the different speaking patterns of family members living in the same home and even accents of different users.

Now it only makes sense that those two parallel streams of technology soon will be merged inside truck cabs all across North America and beyond. Trucking is a business that, at the driver level, requires the full attention of the person in the cab for the safe operation of a large and not-so-nimble vehicle traveling at high speed. Currently, for trucks equipped with GPS-enabled mapping and traffic systems, systems that monitor truck performance and driver performance, and various load management and business systems, the only way a driver can access all that information safely is to pull off the road, somewhat defeating the efficiency gains such systems are supposed to produce. Even then, the driver has to log into multiple systems and think through all that information to determine, hopefully, what the big picture up ahead really is. 

Why not use algorithms to analyze all that data quickly and accurately and then report it to the driver using the kind of voice assistant technology we’re seeing in Alexa and Siri? That would give drivers quicker, easier, and safer access to arguably more accurate and properly analyzed information, all without taking their hands off the wheel or their eyes off the road. 

I’ve spent five years in the industry working on ways to track critical truck and driver performance data and load management data, and then determine how best to report it to drivers, dispatchers, and managers. Our goal is to improve efficiency and business performance while also maintaining or improving on today’s record-high levels of safety. And it’s obvious to me that we will be seeing advanced voice activated data management and analytical systems introduced into truck cabs pretty soon.
It’s both a safer way and a faster, more efficient way of getting important data to drivers. 

There’s also a benefit to dispatchers, managers, or even C-level executives at a big trucking company being able to walk into their offices at any point in the day and asking in plain language for fleet data and having a system provide real-time data to them. They won’t have to log on to four or five different systems and analyze different dashboards to get a good understanding of what happened in their network overnight, or what’s happening right now in the field.

This just makes a lot of sense both for drivers and for fleet managers.

And it can happen sooner than you probably think. Multiple vehicle manufacturers already have been offering limited voice-activated interfaces in their passenger vehicles that allow drivers or occupants do things like select the radio station they want to hear, to listen to specific songs or genres of music, to change passenger cabin environment controls, to answer their phone hands free, and to get audio directions to their destination.  There are even manufacturers dedicating teams of engineers to integrating Alexa into some of their car models due out in 2018. It’s time we integrate the same technologies into trucks, too. 

It’s just a matter of coupling algorithmically derived results to Alexa-type voice activated smart speaker technology. Instead of being able to ask things like “what’s the weather today,” which you can do already with Siri on an iPhone and other systems that are based on publicly available information, you’ll be able to ask specific questions about your own truck or fleet or about the status of specific products being shipped. The system will be tied directly into your own company’s data and your own truck’s data as well so you’ll have almost instant access to real-time data that today you can only see the next day, or a few hours later. And the technology will simply speak that information to you in an easy-to-understand way.

A team from Omnitracs recently won a hackathon displaying how the technology can be used to make our fleet managers better informed. That lets them operate safer and more efficient fleets. And how it could let our drivers operate more efficiently trucks and do it more safely. The hackathon was sponsored by Amazon as well as Automated Insights, which provides the open APIs for natural language generation. 

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