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Robocars As the Best Way Geeks Can Save the Planet
Brad Templeton writes "I (whom you may know as EFF Chairman, founder of early
dot-com Clari.Net and rec.humor.funny)
have just released a new series of futurist
essays on the amazing future
of robot cars, coming to us thanks to the DARPA Grand Challenges.
The computer driver is just the beginning — the
essays detail how robocars can
enable the cheap electric car, save millions of lives and trillions
of dollars, and are the most compelling thing
computer geeks can work on to save the planet. Because robocars can refuel, park
and deliver themselves, and not simply be chauffeurs, they end up changing
not just cars but cities, industries, energy, and — by removing
dependence on foreign oil — even wars. I, for one, welcome our new robot overlords."
(More below.)
Templeton continues: "The key realization is that while the safety and timesavings that
come from having computers as chauffeurs is very important and can
save a million lives every year, a number of interesting consequences
come from the ability of robocars to drive themselves while vacant.
This allows them to deliver themselves to us on demand, to park
themselves and to refuel/recharge themselves. On-demand delivery makes
car sharing pleasant and allows the use of "the right vehicle for
the trip" on most trips. Self-refueling means the people using
cars no longer need care about range or how common fueling stations
are, enabling all sorts of novel energy systems with minimal "chicken
and egg" problems. Because passengers don't care about the range
of their taxis, battery weight and cost are no longer issues in
electric cars and scooters."
The AI is not so powerful. Most animals can navigate in traffic of their own kind, even insects. But no, it's no tiny project -- but it's a tractable large project.
Two problems with a lot of robot navigation systems that use visual processing are handling the differences in light at different times of the day, and handling a dynamic environment.
The environment can look very different even just comparing morning and evening, not touching night or times of the year. This makes following a path that you learned under one set of conditions look like it's a different path in another set of conditions. It's not a problem in indoor environments that have controlled lighting, but in a "real" scenario, it's not a toy problem.
I intend to live forever, or die trying. - Groucho Marx
While he does make this conclusion about U.S. data, he is fair and continues his search to other parts of the world like Europe and Asia. From this page: