Still No viable method of transporting data from autonomous cars tests

Driverless Cars

Behind the scenes at locations around the world the auto makers are running tests on autonomous cars for literally thousands of hours. The industry has poured more than $80 billion into R&D on autonomous cars over the last four years, so they are serious about making this happen.

Those of us working on these tests have one overwhelming challenge: how to manage all the data that gets generated during the tests. One eight-hour shift can create more than 100 terabytes of data. In a week of testing multiple cars, we’re talking about petabytes of data. And often — at rural testing centers, for example — Internet bandwidth speeds are simply insufficient to ensure that the data reaches our data centers in North America, Europe and Asia at the end of the test day.

Autonomous car

Right now, we have two main ways to transport data back to a data center. They are both cumbersome, but have different plusses and minuses. Until advances in technology make these challenges easier to manage, here’s what we do today:

  • Connect the car to the data center. Test cars generate about 28 terabytes of data in an hour and it takes 30 to 60 minutes to offload that data by sending it to the data center over a fiber optic connection. While this is a time-consuming option, it remains viable in cases where the data gets processed in somewhat smaller increments.
  • Take/ship the media to a special station. In many situations the data loads are too large and the fiber connections unavailable (e.g., at geographically remote test locations such as deserts, ice lakes and rural areas) to upload data directly from the car to the data center. In these cases we remove a plug-in-disk from the car and take it or ship it to a “Smart Ingest Station” where the data is uploaded to a central data lake. Because it only takes a couple of minutes to swap out the disks, the car stays available for testing. The downside of this option is we need to have several sets of disks, so compared to Option 1 we are buying time by spending money.

In three to five years we may get to the point where both options are outmoded by advances in technology that make it possible for the computers in the car to run analysis and select the needed data. If the test car could isolate the test-car video on, for example, right-hand turns at a stop light, the need to send terabytes of data back to the main data center would be alleviated and the testers could send these smaller data sets over the Internet.

Of course, we’re several years away from having such a capability. In the past year, IBM and Sony have been working on a 330 terabyte tape drive that promises faster and more resilient data storage in a form factor that can fit in the palm of your hand. Once such products are commercialized, it should make our lives a bit easier.

Ultimately, we’d like the ability to move our various equipment easily in and out of hotel rooms and carry it on plane trips in our pockets or briefcases. Today, the equipment is often clunky and hard to move around. While technology can help, we have to be realistic and understand the data challenges surrounding autonomous cars are likely to increase exponentially.  The challenges may grow, but at least sometime soon the gear we use won’t be so cumbersome that our muscles ache at the end of the day.

Threats and Opportunities in Travel and Transportation from Digital Transformation

Digital Transformation

In travel and transportation most companies today don’t look at customer journeys as a collaborative exercise. They consider their job done when passengers are delivered safely to their appointed destination for their segment. A railway, for example, may only care that it has moved passengers safely from station A to station B. It ignores the fact station B is an airport, and the passengers it dropped off are actually headed to dozens of different destinations.

To deliver real value to customers, companies need to surmount the cultural and technical obstacles to data sharing to create a true transportation ecosystem. The idea of bridging systems, breaking down silos and sharing data with others raises the fear of companies losing their individual value propositions. The reality, however, is that being part of a connected transportation platform will generate more value than it destroys and will create entirely new opportunities for companies that never before existed. What’s needed is a digital enablement strategy.

Digital Transformation in Travel, Transportation

Looking across a chain of events

Digital transformation is built around an information architecture that enables companies to look across a chain of travel events for an individual customer or package to identify problems, predict the impact, and automatically develop and execute solutions that keep passengers and freight moving.

New services can be built around a platform like this that help companies differentiate their offerings or add value in new ways, through mobile tracking solutions, or by using analytics to improve warehousing fulfillment and distribution. For examples, sensors in a refrigerated freight car that can sense an elevation in temperature could trigger a maintenance request to repair a problem or move cargo to another car before it spoils.

Passenger transportation companies can tap into these same tools to find ways to extend their brands and expand into the multiple modes of transportation available to passengers. Digital enablement helps companies understand the full passenger journey and allows for a seamless approach, even if the company is not part of the entire chain of events. A hotel notified that incoming guests are experiencing delays could offer weary travelers an added comfort or convenience as a way to differentiate their customer service.

Capabilities like this don’t require wholesale integration. They can be developed and delivered through loosely connected systems that share selected data, understand the most important attributes of a customer’s journey, and have the awareness to detect issues, the context to recognize the impact and the intelligence to take action.

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