Volkswagen has given engineering a black eye

October 05, 2015 // By Bill Schweber
Bill Schweber ponders the mess Volkswagen has got themselves in and considers the risk you will not be able to trust the data from a trillion sensors. He praises the age-old wisdom of the crosscheck and approaching a result from two or more different directions.

You are undoubtedly aware of the absolute mess that Volkswagen is in, because they deceitfully and deliberately deployed software that switched between various diesel engine- and exhaust-control management algorithms when the car was on the road versus when it was undergoing formal evaluations on the test bed. The apparent intention was unwind the trade-off between meeting lower emission mandates but at the cost of lower mileage in the tests, yet also do well in mileage results on the road.

Sad to say, many engineers were undoubtedly involved, and that's the kind of ethics black eye that our profession does not need.

I was, however, intrigued by how this is scheme was discovered. As noted in an article in The Wall Street Journal, "VW Emissions Problem Was Exposed by West Virginia University Researchers," some students and a professor were working on a small grant-funded project to review some diesel-emissions data and they did something basic and obvious: they bought a real car and borrowed some others, hooked up their sensors and instrumentation directly to the tailpipe, and took emissions data from actual on-the-road driving. When their results differed significantly from the official data, they checked again, and that's how the deceit began to unravel.

This brings me to my concern, and it is not just related to cars, VW, or similar situations. We have so much sophistication and complexity in our analysis of signals coming from so many sensors and translated into data that it is easy to forget to ask some basic questions: how do we know this answer is correct? Are there any independent crosschecks we can do? Can we first measure something directly and do a basic analysis of the data, using rough calculations and estimates, to see if the numbers are likely correct?

I had a glimmer of this situation several years ago, when I was a judge at a local high-school science fair. Many of the projects were straightforward, such as growing plants under different lights, or building some mechanical devices; you could follow the chain of reasoning, analysis, and results; there was a certain provenance, you might say.

Next: Go back to the sensor source