Today when I use my GPS to plan a trip, I don’t tell anyone
else about the details so they can include that in their planning. And that
means I don’t have information about other people either, other than
statistical predictions, or real-time traffic data.
So if I ask when is the best time to go to dinner so I don’t
have to wait in line? Or when do I need to leave to get my preferred seat at
the movie? Or how early do I have to leave to miss rush hour (and that’s a big
deal when we are trying to get past New York City going to the Adirondacks J)? The GPS doesn’t help
me.
In the 4-Dimensional Map I will enter my Planned Path, and
my Personal Transport Vehicle will update it as we progress toward my
destination. If enough people do this then I will have very good answers to all
those questions. Actually the Autonomous Vehicles will enter the data for me,
so we should have very good data.
A study 20 years ago showed that executive’s calendars were
only correct about ½ the time in predicting where they would be. But as people
become more dependent on smart-devices, our plans and schedules become more
detailed and more accurate, even tracking changes.
As more systems join the 4-Dimensional Map we will get even
better plans – more about this if future posts.
The 4-Dimensional Map can predict how early I need to leave
to be sure I get to my 9 am meeting on time.
Scheduling will evolve to include
probabilistic estimates. For example, if I need to be in the O’Hare Hilton for
a 9 am meeting on Tuesday with at least a 90% probability of getting there on
time, I can take the 7 am shuttle from Newark Liberty airport. But if I want to
be 95% sure I need to take the 6 am shuttle, and if I need to be 99% sure, I need
to go out Monday night and stay overnight in the O’Hare Hilton – these
calculations depend on all sorts of variables, including weather, on-time
statistics for the flights, strikes against the airline, maintenance issues,
how crowded the highways and airways will be, crowds at sports events in nearby
arenas, shore traffic on summer weekends, etc.
The Autonomous Vehicle hierarchy means timing is important
so that my Personal Mobility Vehicle doesn’t have to wait to join a Convoy,
and that there is enough space on the Convoy Vehicles for me. Fortunately, this will be
transparent to me because the Autonomous Vehicles automatically take this into
account, as long as I tell my Personal Transport Vehicle where I want to go and
when.
Planning and prediction become more important as we add the
variety of Vehicles in the Autonomous Vehicle Hierarchy: Convoy Vehicles of
different sizes and speeds, Personal Mobility Vehicles carrying people and
other Autonomous Vehicles, and Mini-Mobility Vehicles carrying everything from
fruit to medications.
For example, a Mini-Mobility Vehicle bringing my morning
pills from the drug store may use several different Personal Mobility Vehicles
and Convoy Vehicles on its rapid path to my breakfast table.
Nested Vehicles lead to nested Local Maps and nested
Cloudlets. The Convoy Vehicles are communicating with each other so the Convoy
functions properly. Within the Convoy, the Autonomous Vehicles being carried along
are communicating with each other as a Cloudlet to maneuver within the Convoy
Vehicles, and with the Convoy Vehicles to determine which Convoy Vehicle to be
in to exit at the appropriate station, as part of En Route Sequencing. Nested Convoy
Vehicles make this even more interesting.
In later Posts I will talk about optimizing paths through
this complex rapidly moving web.
Next I want to talk about an application to help people who
are wheelchair bound: an Autonomous Toileting System.
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