The University of Washington Bothell is building a self-driving bicycle that can travel 30 MPH with a 15-mile range. The team claims the self-driving bicycle “could get riders from A to B faster than a self-driving car, and at a tenth of the cost.”

Unlike Google’s April Fool’s Day video (watch below) that showed a self-driving bicycle navigating its way around Amsterdam, the work being done by University of Washington Bothell professor Tyler Folsom is no joke.

Folsom and crew are trying to ensure bicycles are included in the movement toward self-driving vehicles. The UW Bothell team is building a self-driving tricycle that can travel at speeds up to 30 MPH with a 15-mile range. The team claims the self-driving bicycle “could get riders from A to B faster than a self-driving car, and at a tenth of the cost. Not only that, the self-driving bike helps combat climate change.”

Folsom tells Robotics Trends the self-driving bicycle is based on a stack of five Arduinos with no operating system. The open-source documentation and C/C++ code can be found on GitHub. The self-driving bicycle has object recognition that’s based on an array of sonar range finders. Here’s more from Folsom on how the self-driving bicycle sees its environment:

“Localization combines GPS with dead reckoning. It uses a fuzzy filter to reconcile the two estimated positions. Dead reckoning is based on speedometer and magnetometer. In the future, localization may be extended to include accelerometer, optical odometry and visual lane edge detection. A Raspberry Pi handles the visual tasks.

“To get from point A to point B, an Arduino reads an SD card with digital maps and selects the one with the closest latitude and longitude. That map is a directed graph connecting intersections. The bike computes the shortest path to the road network, then finds the shortest route to the destination.”

In the video atop this page, the self-driving bicycle executes a “circle test” outdoors for the first time. Making the self-driving bicycle go, turn and stop is the first step toward higher-level, expanded paths, says Folsom, who adds that the self-driving bicycle uses the minimum amount of computational power.

A self-driving bicycle being tested inside the lab at University of Washington Bothell. (Credit: UW Bothell)

“We’re using things much less powerful than a smartphone. Part of the concept is that you don’t have to spend as much money as the big car companies are spending, “says Folsom. “My contention is you don’t need all that much processing power to make autonomy happen.”

The self-driving bicycle is just a prototype at this point, but it recently received a $75,000 grant from Amazon Catalyst that has allowed Folsom to form a small team. Folsom says the goal is to keep the cost of each self-driving bicycle under $10,000. That’s a hefty price tag for consumers to spend on a bike, but Folsom points out it could make a good transit alternative as 100 self-driving bikes would cost the same a $1 million bus. So if this project gets off the ground, consumers might not be the main target.

“The big thing for me is the effect this could have on global warming. If we can push transportation in this direction – very light vehicles – it’s a major win for the environment,” says Folsom, who rides an electric bike and drives an electric car himself. “I want to have the technology that lets people make that choice if we decide, yes, by the way, survival would be a nice thing.”

Google’s April Fool’s Day joke introducing a self-driving bicycle in the Netherlands.

Would you purchase or ride a self-driving bicycle? You’d lose all the health benefits that come with pedaling a bicycle, but this could certainly cut down on getting stuck in traffic, if you have a short commute, and help the environment. Either way, an interesting project to keep an eye on.

On a related note, you might remember an incident we covered about a cyclist’s interesting encounter with an indecisive Google self-driving Lexus SUV while riding in Austin, Texas. Gregg Tatum, an avid cyclist who rides about 6,000 miles per year, shared his story with us:

“Recently, near the end of one of my rides, I was approaching an intersection in my neighborhood that is signed as a four-way stop. As I slowed and prepared to stop, a vehicle entered the same intersection from the street to my left. The white Lexus SUV was unusual as it was adorned with a prominent roof-top camera as well as a variety of other sensors attached to various parts of the car. Closer inspection revealed that it was labeled as a ‘Google Self-driving Car.’

“The car arrived at the stop line a fraction of a second before I did and had the legal right-of-way. Not wanting to unclip my shoes from the pedals, I performed a maneuver called a track-stand in which a rider comes to a stop and then simply balances the bike without putting a foot down. As I waited for the car to proceed, I noticed that there were two occupants inside that appeared to be observers/testers.

Related: 8 Reasons Self-Driving Cars Are a Cyclist’s Best Friend

“The car remained motionless for several seconds and I continued to maintain my balance without moving. As the car finally inched forward, I was forced to rock the handlebars to hold my position. Apparently, this motion was detected by one of the sensors and the car stopped abruptly. I held the bike in balance and waited for another several seconds and the cycle repeated itself – the car inched forward, I shifted my weight and re-positioned the bars and the car stopped. We did this little dance three separate times and the car was not even halfway through the intersection. I noticed at one point that the testers were laughing and apparently typing code into a terminal to ‘teach’ the vehicle how to deal with the situation.

“The car finally rolled on and I proceeded on my ride – it was an interesting experience and I noticed that I actually felt safer dealing with a robotically-operated vehicle than one with a human driver.”

Now, Google’s self-driving cars do have patented technology for cyclist hand detection. The car’s sensors will notice a cyclist among other objects and vehicles on the road. It then measures the distance between the cyclist’s hand and head to decide whether a cyclist is turning or stopping. The algorithm will also look at the angle at which the cyclist’s elbow is bending, and the size and shape of the cyclist’s hands, arms and head.