Probably the highest profile fatal crash involving a driverless car occurred in Arizona in March 2018.
An Uber test car, in autonomous mode but with a safety driver, hit a 49-year-old homeless woman in the city of Tempe.
Elaine Herzberg was walking with a bicycle and not on a crossing. It was the first reported fatal crash in the US involving a self-driving vehicle and a pedestrian.
Fast forward nearly a year and the University of Michigan has unveiled a new project to predict pedestrian movements with greater accuracy.
“Prior work in this area has typically only looked at still images,” said Ram Vasudevan, assistant professor of mechanical engineering. “It wasn’t really concerned with how people move in three dimensions.”
By studying things like gait pace, foot placement and the symmetry of arms and legs, the team attempt to predict the future locations of one or several pedestrians up to 50 yards from the vehicle.
“If a pedestrian is playing with their phone, you know they’re distracted,” said Vasudevan. “Their pose and where they’re looking is telling you a lot about their level of attentiveness. It is also telling you a lot about what they’re capable of doing next.”
Previously, the most notorious driverless crash was also in the US, in 2016, when a Tesla Model S in autopilot mode smashed into a truck’s trailer, killing the car’s 40-year-old driver.
There have been numerous close shaves too.
Just last week in St. John’s, Canada, a driverless car reportedly set off at high-speed down a residential street, jumped a snow bank and slammed into a nearby garage.
Incredibly, no one was hurt. The Royal Newfoundland Constabulary (RNC) is investigating.