MIT has developed and tested a self-driving scooter. Let us see about it in this video.
At MIT’s 2016 Open House last spring, more than 100 visitors took rides on an autonomous mobility scooter in a trial of software designed by researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), the National University of Singapore, and the Singapore-MIT Alliance for Research and Technology (SMART).
The researchers had previously used the same sensor configuration and software in trials of autonomous cars and golf carts, so the new trial completes the demonstration of a comprehensive autonomous mobility system. A mobility-impaired user could, use a scooter to get down the hall and through the lobby of an apartment building, take a golf cart across the building’s parking lot, and pick up an autonomous car on the public roads.
The new trial establishes that the researchers’ control algorithms work indoors as well as out
The researchers’ system includes several layers of software: low-level control algorithms that enable a vehicle to respond immediately to changes in its environment, such as a pedestrian darting across its path; route-planning algorithms; localization algorithms that the vehicle uses to determine its location on a map; map-building algorithms that it uses to construct the map in the first place; a scheduling algorithm that allocates fleet resources; and an online booking system that allows users to schedule rides
Using the same control algorithms for all types of vehicles — scooters, golf carts, and city cars — has several advantages. One is that it becomes much more practical to perform reliable analyses of the system’s overall performance.
Furthermore, with software uniformity, information that one vehicle acquires can easily be transferred to another. Before the scooter was shipped to MIT, it was tested in Singapore, where it used maps that had been created by the autonomous golf cart.
Finally, software uniformity means that the scheduling algorithm has more flexibility in its allocation of system resources. If an autonomous golf cart isn’t available to take a user across a public park, a scooter could fill in; if a city car isn’t available for a short trip on back roads, a golf cart might be.