Self-Driving Cars Should Learn to Communicate with Cyclists

Category Computer Science

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In order to reduce the number of cyclist accidents on the roads, researchers from the University of Glasgow are suggesting that future generations of self-driving cars should learn to interact with cyclists. This involves the AVs having displays to signal their intentions, and cyclists wearing smart glasses that also indicate what the vehicle is doing.

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Future generations of self-driving cars should learn the language of cyclists to help them safely share the roads with bikes, new research suggests.Human-computer interaction specialists from the University of Glasgow are highlighting the need for new systems in autonomous vehicles (AVs) capable of replicating the complex social interactions between human car drivers and cyclists on U.K. roads.

The paper is titled "Keep it Real: Investigating Driver-Cyclist Interaction in Real-World Traffic," and it will be presented at the ACM Conference on Human Factors in Computing Systems in Germany next week. In it, the team describes how they studied the many ways drivers and cyclists directly and indirectly communicate with each other in real-life situations on the road.

The research team found that drivers tend to be more patient than machines when it comes to communicating with cyclists.

Their findings form the basis of a new series of recommendations on how AVs should behave safely around cyclists in the decades to come, where drivers will be less actively engaged in their journeys. For AVs to work safely in human traffic, they must behave appropriately and understand human communications.

Self-driving cars could better signal their intentions with displays integrated onto their exteriors, the team suggest. A series of traffic-light-like colored LEDs on the cars' edges could to display animations which signal their intentions to maneuver, slow or speed up, or give way, helping cyclists to better interpret the AVs' intentions and respond appropriately.

The orange LEDs displayed on the vehicles indicate that the right of way is under negotiation.

Cyclists could also wear new types of "smart glasses" that display information on AVs' intentions by allowing the cars to communicate directly with any cyclists around them. AVs could signal that the right of way is up for negotiation, for example, with orange lights displayed on the vehicle and a vibration sent to the cyclists' glasses as a non-verbal message.

Professor Stephen Brewster, of the University of Glasgow's School of Computing Science, led the research. He said, "Cars and bikes share the same spaces on the roads, which can be dangerous—between 2015 and 2020, 84% of fatal bike accidents involved a motor vehicle, and there were more than 11,000 collisions.

AVs aim to reduce the number of cyclist accidents on the roads.

"There has been a lot of research in recent years on building safety features into autonomous vehicles to help keep pedestrians safe, but comparatively little on how AVs can safely share the road with cyclists.

"That's a cause for concern as AVs become more commonplace on the roads. While pedestrians tend to meet AVs in highly controlled situations like road crossings, cyclists ride alongside cars for prolonged periods and rely on two-way interactions with drivers to determine each other's intentions.

The research will be presented at the ACM Conference on Human Factors in Computing Systems in Germany.

"It's a much more complicated set of behaviors, which makes it a big challenge for future generations of AVs to tackle. Currently, self-driving cars currently offer very little direct feedback to cyclists to help them make critically important decisions like whether it's safe to overtake or to switch lanes. Adding any guesswork to the delicate negotiations between car and bike has the potential to make the roads less safe." .

The research was conducted in the city of Glasgow and studied 414 separations between drivers and cyclists.

Credit: University of Glasgow .

The team set out to develop potential solutions to the problem by setting up two observational studies of road traffic in and around the city of Glasgow to learn more about how road users interact.

Firstly, they watched 414 separations between car drivers and cyclists—the moments of interaction on a road, such as passes and turns. The second part of the study used surveys and interviews with drivers and cyclists.

The team also used surveys and interviews with drivers and cyclists to learn more about their interactions.

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