From IEEE Spectrum (Institute of Electrical and Electronics Engineers):
“Bicycles are probably the most difficult detection problem that autonomous vehicles face …
That’s why the detection rate for cars has outstripped that for bicycles in recent years. Most of the improvement has come from techniques whereby systems train themselves by studying thousands of images in which known objects are labeled. One reason for this is that most of the training has concentrated on images featuring cars, with far fewer bikes. …
Automated cyclist detection is seeing its first commercial applications in automated emergency braking systems (AEB) for conventional vehicles, which are expanding to respond to pedestrians and cyclists in addition to cars. …
AEB systems still suffer from a severe limitation that points to the next grand challenge that AV developers are struggling with: predicting where moving objects will go.
… cyclists movements are especially hard to predict.
That means it may be a while before cyclists escape the threat of human error, which contributes to 94 percent of traffic fatalities, according to U.S. regulators. “Everybody who bikes is excited about the promise of eliminating that,” says Brian Wiedenmeier, executive director of the San Francisco Bicycle Coalition. But he says it is right to wait for automation technology to mature.
Another issue, too, that we need to start thinking about: Will pedestrians and cyclists be constrained from using streets without strict controls over where they can go?
If it’s possible for a casual pedestrian or cyclist to go wherever they want, confidently knowing that vehicles will be programmed to stop or avoid them, will people start to “torment cars”? And will there then be legal constraints to stop them by limiting the way the streets can be used – and who has priority?