I realize you are kidding, but I spent thousands of hours in the early nineties thinking about automating driving in cities. My conclusions were:
* it would easier to start with night driving since there are more obvious indicators and less visual noise, then generalize to daytime.
* collision avoidance would be better done with sonar, as there is meaningful doppler information available in addition to distance.
* Curb mapping would be handled with sonar too, but visuals are needed for identifying the lines in the road.
a far cry from the intelligent skateboards in Snow Crash, but essentially dealing with the same problem domain.
IIRC there were successful demonstrations of intelligent cruise controls that kept a distance from the car ahead rather than simply tracking speed, and very intelligent cruise controls that could entirely take over normal highway driving.
Exceptional situations, such as what to do when a piano falls off a flatbed in front of you, are of course unpredictable and will prevent commercialization of the VICC: The potential liabilities are infinite.
I understood the DOD test to be across some rough desert rather than on roads, so the biggest problem is smart obstacle avoidance. Sort of like the science fair robot mazes, scaled up, and not on a smooth floor. Can't have your robot falling off a cliff, unless of course it's built to take falling off a cliff -- like those huge self-propelled beach balls they were talking about deploying on mars.
On Sun, 6 Feb 2005 19:37:28 -0800 (PST), Jack [email protected] wrote:
Of course you'd need to add mundane things like crash avoidance, lane changing, turning. All part of the knowledge base based on system inputs. Piece of cake. I could do this in six months with a team of 10 coders. Also, I'm using very cheap labor, probably Rusisans.
Brian Densmore