Autonomous vehicles are disrupting traditional business models within one of the world’s largest industries and it’s happening much sooner than anyone expected. What are the forces behind the push for self-driving cars? Is the necessary road infrastructure in place, or do we still need to build it? What happens, if an autonomous vehicle needs to make life-or-death decisions? What, in effect, are the possibilities – and the risks – involved when humans hand over control to robo-chauffeurs?
“Four years“. That was the answer given by Jensen Huang, CEO of Nvidia early last year when he was asked how long it would take for artificial intelligence to enable fully automated cars. And then a funny thing happened. Suddenly, self-driving vehicles began to crop up on public roads all over the place.
At the Barcelona Motor Show in May, Audi unveiled the 2018 Audi A8, which it claimed as the world’s first production car to offer Level 3 autonomy. Level 3 means the driver doesn’t need to supervise things at all, so long as the car stays within certain guidelines. In Audi’s case that means never driving faster than 60kmph (37 mph). Audi has billed this feature as the AI Traffic Jam Pilot.
In the US, Las Vegas became the first city in America to have a self-driving shuttle operating in real-time traffic. However, on its first day of service in downtown Las Vegas, the shuttle collided with a truck. The driverless bus was not able to back off when the truck was backing into the alley so, technically at least, the human driver caused the crash, not the shuttle.
In September, General Motors showcased the third generation of its autonomous Chevrolet Bolt, which they developed jointly with recently-acquired Cruise Automation, headquartered in San Francisco. Kyle Vogt, the CEO of Cruise Automation, called it the “first production model self-driving car in the world”.
The self-driving vehicle is something that’s time has come much faster than anyone expected. For Jensen Huang and Nvidia it means big bets are paying out even sooner than they’d hoped. The company has invested heavily in research involving machine learning, which Huang says is the “bottom-up approach to artificial intelligence” – and probably the most promising technology today. Machine learning requires the processing of huge amounts of data, and as it turns out, the company’s computational graphics processing units (GPUs) can do the job both faster and using less energy than traditional central processing units (CPUs) that power most desktops, laptops and mainframe computers today. Weiterlesen