Tech start-up creates self-driving SCOOTERS that can drive themselves directly to commuters and then back to charging points when the journey is over
- The technology could mean scooters and bikes aren’t left scattered around
- They would return themselves to charging points or train stations, for example
- Uber and a company called Tortoise are working on the self-driving technology
Self-driving scooters could soon be whizzing around without riders in a tech development designed to improve cities’ transport-sharing networks.
A California-based start-up, Tortoise, is working on self-driving technology with which bikes and scooters drive themselves home after someone has used them.
And taxi-hailing app Uber announced earlier in the year that it was working towards the same goal.
Set to launch next month, the initiative is a progression for the temporary bike, scooter and Segway hire schemes which already exist around the world.
It could mean fewer of the bikes and scooters are left lying around in obscure places and that they all return to charging stations or busy areas when not in use.
The self-driving scooter technology would drive it back to a charging point or a transport hub if someone has left it somewhere it’s not likely to be needed
California-based startup, Tortoise, is working on the technology and said the scooters would drive themselves on uncomplicated routes where there aren’t too many people
Tortoise’s scheme would help to keep the arrangements running smoothly and avoid the bikes and scooters ending up scattered around the city.
If able to drive themselves back to docking stations it would be easier for the operators to recharge, replace and retrieve the gadgets, CityLab reported.
Although the technology isn’t in use yet, video has surfaced online of a scooter driving itself with the help of stabiliser wheels.
And the company is hoping to put its tech into use in the city of Peachtree Corners, Georgia, to see if it can ease congestion at lunchtimes.
It plans to roll out the technology there in November, as well as in two as-yet-unnamed cities in Europe.
Tortoise said its technology would try to travel on simple routes where there aren’t many people.
If the scooter fell over – like the technology’s namesake – it wouldn’t be able to get back up on its own and would need an employee to go and pick it up.
Tortoise said last month that it was aiming to put the technology into Peachtree Corners, Georgia, to begin with
A spokesperson told CityLab: ‘The system would automatically ping the operator letting them know human intervention is required.
‘Because we’ll be optimizing for smooth routes with not a lot of foot traffic (when possible) the hope is that this isn’t as huge of an issue as it currently is with scooters.’
The benefit of having the scooters drive themselves would be the ability of them to return to places where they will be useful.
For example, if someone used one to get home from the city centre and left it near their house further out of town, it could return itself to a train station or busy area where people might be more likely to use it.
A risk, however, is that the technology is expensive and therefore a target for thieves.
A robo-scooter in development by Segway will be worth more than $1,000 (£769).
Uber revealed in January this year that it was developing self-driving bikes and scooters which could return themselves to charging stations.
Uber said it hoped it could avoid the problem of dumped scooters and bikes that have plagued current services.
It remained tight-lipped on the project, but said it would bring ‘sensing and robotics technologies’ to shared bikes and scooters.
‘The New Mobilities team at Uber is exploring ways to improve safety, rider experience, and operational efficiency of our shared electric scooters and bicycles through the application of sensing and robotics technologies,’ it said at the time.
HOW DO SELF-DRIVING CARS ‘SEE’?
Self-driving cars often use a combination of normal two-dimensional cameras and depth-sensing ‘LiDAR’ units to recognise the world around them.
However, others make use of visible light cameras that capture imagery of the roads and streets.
They are trained with a wealth of information and vast databases of hundreds of thousands of clips which are processed using artificial intelligence to accurately identify people, signs and hazards.
In LiDAR (light detection and ranging) scanning – which is used by Waymo – one or more lasers send out short pulses, which bounce back when they hit an obstacle.
These sensors constantly scan the surrounding areas looking for information, acting as the ‘eyes’ of the car.
While the units supply depth information, their low resolution makes it hard to detect small, faraway objects without help from a normal camera linked to it in real time.
In November last year Apple revealed details of its driverless car system that uses lasers to detect pedestrians and cyclists from a distance.
The Apple researchers said they were able to get ‘highly encouraging results’ in spotting pedestrians and cyclists with just LiDAR data.
They also wrote they were able to beat other approaches for detecting three-dimensional objects that use only LiDAR.
Other self-driving cars generally rely on a combination of cameras, sensors and lasers.
An example is Volvo’s self driving cars that rely on around 28 cameras, sensors and lasers.
A network of computers process information, which together with GPS, generates a real-time map of moving and stationary objects in the environment.
Twelve ultrasonic sensors around the car are used to identify objects close to the vehicle and support autonomous drive at low speeds.
A wave radar and camera placed on the windscreen reads traffic signs and the road’s curvature and can detect objects on the road such as other road users.
Four radars behind the front and rear bumpers also locate objects.
Two long-range radars on the bumper are used to detect fast-moving vehicles approaching from far behind, which is useful on motorways.
Four cameras – two on the wing mirrors, one on the grille and one on the rear bumper – monitor objects in close proximity to the vehicle and lane markings.