Self-Driving Cars Have a Bicycle Problem
Bikes are hard to spot and hard to predict
By PETER FAIRLEY
Feb 24 2017
Robotic cars are great at monitoring other cars, and they’re getting better at noticing pedestrians, squirrels, and birds. The main challenge, though, is posed by the lightest, quietest, swerviest vehicles on the road.
“Bicycles are probably the most difficult detection problem that autonomous vehicle systems face,” says UC Berkeley research engineer Steven Shladover.
Nuno Vasconcelos, a visual computing expert at the University of California, San Diego, says bikes pose a complex detection problem because they are relatively small, fast and heterogenous. “A car is basically a big block of stuff. A bicycle has much less mass and also there can be more variation in appearance — there are more shapes and colors and people hang stuff on them.”
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.
Consider the Deep3DBox algorithm presented recently by researchers at George Mason University and stealth-mode robotic taxi developer Zoox, based in Menlo Park, Calif. On an industry-recognized benchmark test, which challenges vision systems with 2D road images, Deep3DBox identifies 89 percent of cars. Sub-70-percent car-spotting scores prevailed just a few years ago.
Deep3DBox further excels at a tougher task: predicting which way vehicles are facing and inferring a 3D box around each object spotted on a 2D image. “Deep learning is typically used for just detecting pixel patterns. We figured out an effective way to use the same techniques to estimate geometrical quantities,” explains Deep3DBox contributor Jana Košecká, a computer scientist at George Mason University in Fairfax, Virginia.
However, when it comes to spotting and orienting bikes and bicyclists, performance drops significantly. Deep3DBox is among the best, yet it spots only 74 percent of bikes in the benchmarking test. And though it can orient over 88 percent of the cars in the test images, it scores just 59 percent for the bikes.
Košecká says commercial systems are delivering better results as developers gather massive proprietary datasets of road images with which to train their systems. And she says most demonstration vehicles augment their visual processing with laser-scanning (ie lidar) imagery and radar sensing, which help recognize bikes and their relative position even if they can’t help determine their orientation.
Further strides, meanwhile, are coming via high-definition maps such as Israel-based Mobileye’s Road Experience Management system. These maps offer computer vision algorithms a head start in identifying bikes, which stand out as anomalies from pre-recorded street views. Ford Motor says “highly detailed 3D maps” are at the core of the 70 self-driving test cars that it plans to have driving on roads this year.
Put all of these elements together, and one can observe some pretty impressive results, such as the bike spotting demonstrated last year by Google’s vehicles. Waymo, Google’s autonomous vehicle spinoff, unveiled proprietary sensor technology with further upgraded bike-recognition capabilities at this month’s Detroit Auto Show.