Ford Motor Company and MIT are collaborating on a new research project that measures how pedestrians move in urban areas to improve certain public transportation services.
Ford seems to think so and is working with MIT in order to shuttle in a new form of smarter, on-demand mobility service that can predict where it needs to be at any given time, in order to best help people get around.
From September the Massachusetts Institute of Technology's Cambridge campus will become the testing ground for a fleet of three electric shuttles that will be hitting the roads and walkways around the campus. They can be hailed by students via an app, and students can also use the app to say where they want to be dropped off. However, that's just the start.
"The [shuttles'] onboard sensors and cameras gather pedestrian data to estimate the flow of foot traffic," said Ken Washington, vice president of Research and Advanced Engineering at Ford.
The key to these sensor arrays is LiDAR, which a growing number of carmakers, Ford included, believe is the most accurate way for detecting individual objects and understanding their position relevant to a vehicle, whether those objects are other cars on the road or pedestrians on the pavement or waiting to cross.
However, instead of spotting and avoiding a pedestrian, as with an autonomous vehicle, in these shuttles, they are identifying people in order to understand how and where people move in order to calculate the optimum geographical location to wait at any one time between ride hails. Think an elevator that can "guess" which floor it should stop at before it is summoned.
"Through the mobility-on-demand system being developed for MIT's campus, the Aeronautics and Astronautics Department's Aerospace Controls Lab can investigate new planning and prediction algorithms in a complex, but controlled, environment, while simultaneously providing a test bed framework for researchers and a service to the MIT community," said ACL director Professor Jonathan How.
As well as basing their position based on the density of footfall, the systems underpinning these vehicles will be ale to factor in individual class timetables, weather conditions and changing habits of campus users depending on the semester.
"This helps us develop efficient algorithms that bring together relevant data. It improves mobility-on-demand services, and aids ongoing pedestrian detection and mapping efforts for autonomous vehicle research," said Washington.
){kind=small}
){kind=small}
){kind=small}
){kind=small}
){kind=small}
){kind=small}
){kind=small}
){kind=small}
){kind=small}
){kind=small}
){kind=small}
){kind=small}
){kind=small}
){kind=small}
){kind=small}
){kind=small}
){kind=small}
){kind=small}
 "Ford, Massachusetts Institute of Technology (MIT), Predictive technology, Electric Cars, LIDAR, Autonomous vehicle, self-driving")
){kind=small}
){kind=small}
){kind=small}
){kind=small}
){kind=small}
)
){kind=small}
){kind=small}
)
)
)
)
)
){kind=small}
){kind=small}
){kind=small}
){kind=small}
){kind=small}