February 23, 2019

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The Computer Chauffeur Is Creeping Closer

The Computer Chauffeur Is Creeping Closer
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Still, A.I. is already quietly making driving safer. Beyond the applications now found in new cars, typically in conveniences like the speech-recognition feature of infotainment systems, are the subsystems that make up the packages of safety features common largely in luxury vehicles. Enhancements like night vision, automatic emergency braking and lane keeping all depend on processors that use sensors and computer instructions to warn drivers of danger or act to avoid collisions.

The term artificial intelligence, coined in the 1950s, is something of an unfortunate choice, at least in terms of the automobile. The intelligence within cars — that is, their ability to learn and to apply that knowledge — is far from artificial; it is hard-earned. It comes down to capable electronics, sensors and, especially, extensive training.

“Training is like teaching our kids to drive, with rules, absolutes and best practices,” Glen De Vos, chief technology officer at Aptiv, said in a telephone interview. “Some rules are embedded in the system — never out-drive the free space around the vehicle, obey road signs — but as you move up the spectrum toward accident avoidance, a predictive capacity is necessary.”

Aptiv, a spinoff from Delphi Automotive, an auto industry supplier, builds the data sets that a trained A.I. system depends on. Most of that data is accumulated on the road, acquired in videos to create the basic knowledge bank that computers draw on. In some cases, this work is done overseas to reduce costs, and suppliers can make use of basic image collections — known as a trained data set — obtained off the shelf from market-research organizations.

The key to making the images useful is adding detailed annotation — instructions that specify, “This is a tree, this is a garbage can” — for the object recognition function that is vital to preventing collisions. The work is tedious and until recently has been mainly a manual task, with up to 80 percent of the work devoted to classifying images and cleansing data, said Sachin Lulla, IBM’s automotive leader.

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