Friday, May 27, 2016

Autonomous Vehicle: Top Down or Bottom Up? Trainable Cars

I've posted several times on "self-driving" cars, also known as autonomous vehicles, or driverless-cars.  If I understand, Google and perhaps some others are taking a top-down approach, which seems to involve extensive mapping of roads, signs, etc. etc., feeding the database to the car, and letting the car do its work.  That seems a little reminiscent of some old efforts to teach computers language by inputting vocabulary, grammar rules, etc.  Something similar also seems to have happened with robots.

It strikes me that a bottom-up approach might be more quickly usable, or call it a car with a memory. It's the same principle as teaching robots, learning by doing.

Assume a car with the ability to follow a route, avoiding other vehicles and humans, and with a memory, a trainable car.  Suppose I want my trainable car to take me to the grocery store and back.  I or another driver jumps in the car and drives it to the store, with the car storing the route and the environment of the route in its memory.  Perhaps we repeat the process several times, until the car is satisfied it knows the route.  Then I can get in the car, tell it to take me to the store, and it will do so (or tell me the conditions have changed so it can't).

You may ask: what use is that, I need a car for more than going to the store?  Good point, but my guess is that most driving is done on repetitive routes: that 80 percent of driving is done on 20 percent of routes.  My percentage is much higher than that.  So a trainable car could be rented for such repetitive routes (remember once one trainable car learns the route, the data can be copied to all others).  So Zipcar could train a car to drive to my house, and I could train it to drive to the store, etc.

There are many people who because of age, inebriation, disability, poverty, etc. do not and cannot drive.  I saw a couple women outside the grocery store the other day, waiting with their groceries for a cab to pick them up, too poor to be able to afford owning a car.  For these people a trainable car would be valuable.

For drivers the trainable car would also work, because the 80 percent of the routine routes, the commuting to work, etc. could be handled by the car and allow the "driver" to be on their cellphone, making the roads safer for everyone.

Lastly and perhaps most important, is the fact that data on roads and conditions is flowing up the organization, since a trainable car can transmit updates to the manufacturer which can then flow to the rest of the fleet.  I think that's important: in any structure getting data going up is as important and getting it going down.

What use would a car like that be? 

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