AUTONOMOUS cars perceive the world through a combination of sensors including cameras, radar and LIDAR—a radar-like technique that uses invisible pulses of light to create a high-resolution 3D map of the surrounding area. The three complement each other. Cameras are cheap and can see street signs and road markings, but cannot measure distance; radar can measure distance and velocity, but cannot see in fine detail; LIDAR provides fine detail but is expensive and gets confused by snow. Most people working on autonomous vehicles believe a combination of sensors is needed to ensure safety and reliability. 

Having combined the data from its sensors, the car needs to identify the items around it: other vehicles, pedestrians, cyclists, road markings, road signs and so forth. Humans are much better at this than machines, which have to be trained with lots of carefully labelled examples. One way to obtain them is to pay people to label images manually. Mighty AI, based in Seattle, has an online community of 300,000 people who carefully label images of street scenes, drawing boxes around cars, trees and so forth, for a range of automotive clients. The labelled images are then used to train vision systems used in autonomous cars.

The hardest things to identify are rarely seen items such as debris on the road or plastic bags blowing across a highway. In the early days of Google’s autonomous vehicle project, its perception module could not distinguish a plastic bag from a flying child. Puddles on the road also caused confusion. Combining data from multiple sensors, however, can reveal whether an item in the road is a solid obstacle or not. Cars can also compare their sensor readings with those gathered previously by other cars on the same road, learning from each other’s experiences in a process called “fleet learning”. That may give an edge to first movers who have already racked up thousands or millions of miles of self-driving experience—and the associated gigabytes of driving data.

Read more about driverless vehicles in our special report, Reinventing wheels