- The secret sauce of those computers becoming our chauffeurs is the ubiquitous force of artificial intelligence.
- 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.
If you are among the multitudes sceptical that computers might one day be trustworthy replacements for drivers, consider this: The National Highway Traffic Safety Administration says that 94 per cent of serious crashes are the result of human error.
So yes, computers might prove to be safer at the controls. It’s not a high bar.
The secret sauce of those computers becoming our chauffeurs is the ubiquitous force of artificial intelligence (AI), which is already active in virtual personal assistants and a bank’s customer-service chatbot. But it’s the automobile where AI could have a critical role for the greatest number of people.
Few AI applications carry the responsibility of automotive safety systems, where actions must be carried out in nanoseconds and an ill-considered response might have costly consequences.
Systems that marry microprocessors, sensors and software to make fully driverless cars possible are in the advanced stages of development, but experts say the leap from today’s computer-assisted driving — features like Tesla’s Enhanced Autopilot and Cadillac’s Super Cruise — to fully automated motoring that might render humans optional remains considerable.
SAFETY AND CONVENIENCE
Still, AI 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 spin-off from Delphi Automotive, an auto industry supplier, builds the data sets that a trained AI 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 organisations.
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 per cent of the work devoted to classifying images and cleansing data, said Sachin Lulla, IBM’s automotive leader.
Data is also collected by radar, or lidar, its light-beam equivalent.
A high degree of refinement of the data, covering every possible situation, is vital to assuring that the safety systems don’t issue excessive warnings, an annoyance that might lead a driver to ignore such signals.
The collecting of data to inform automotive AI systems will be greatly improved by a coming generation of connected cars — 50 million communicating wirelessly with each other by 2020 — according to Lulla.