SpectrumIEEE
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.”
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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.
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Volvo began offering the first cyclist-aware AEB in 2013, crunching camera and radar data to predict potential collisions; it is rolling out similar tech for European buses this year. More automakers are expected to follow suit as European auto safety regulators begin scoring AEB systems for cyclist detection next year.
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.”
*****
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.
*****
Volvo began offering the first cyclist-aware AEB in 2013, crunching camera and radar data to predict potential collisions; it is rolling out similar tech for European buses this year. More automakers are expected to follow suit as European auto safety regulators begin scoring AEB systems for cyclist detection next year.
