From an article in Fast Company by Rob Pegoraro.
A different set of eyes is spotting these objects before I do. But they don’t belong to the driver sitting behind the wheel of the Peterbilt that’s taking me for a ride. They’re not eyes at all—instead, an array of sensors attached to the exterior of the rig.
The company behind those sensors, a Pittsburgh startup called Aurora, plans to make this self-driving system a commercial reality—starting with a route that runs between Dallas and Houston—by the end of this year.
And unlike some other firms in the race to make self-driving vehicles happen, Aurora isn’t shooting for something like Tesla’s ambitious but so-far-problematic “full self-driving” capability.
“The proverbial boiling of the ocean in self-driving can be very challenging,” says Sterling Anderson, cofounder and chief product officer at Aurora. “The ocean is too big to boil all at once, solve a specific problem.”
At the starting line in one lane
Aurora has zeroed in on long-haul trucking for a number of reasons: It’s a far larger market than other autonomous-vehicle possibilities, there’s more money to be made, and a general scarcity of drivers. The American Trucking Association estimates that the industry is short of 78,000 drivers and will need to hire another 1.2 million over the next decade. (Bureau of Labor Statistics data show that the median pay for heavy and tractor-trailer drivers in 2023 was $54,320 a year, equating to $26.12 an hour.)
Plus, trucking has an efficiency limit that can’t be fixed by better driver pay or training: how long any one driver can legally stay on the road.
“Those trucks are inherently limited by what humans can do,” Anderson says. For example, a run from Long Beach, California, to Dallas would take two to three days with a human driver taking mandatory rest stops but just 24 hours with a truck driving nonstop on a single tank of fuel.
For the complete article on autonomous trucking lidar CLICK HERE.
