Driverless cars remake the roads–and the economy–once they are fully autonomous. Until then, the gradual integration of elements is useful though not truly revolutionary. Those final few percentage points are tricky, but it appears technologists are headed in the right direction. In a blog post, Brad Templeton, Google driverless consultant, puts the just-completed Delphi cross-country drive into context. An excerpt:

Most of the robocar press this week has been about the Delphi drive from San Francisco to New York, which completed yesterday. Congratulations to the team. Few teams have tried to do such a long course and so many different roads. (While Google has over a million miles logged in their testing by now, it’s not been reported that they have done 3,500 distinct roads; most testing is done around Google HQ.)

The team reported the vehicle drove 99% of the time. This is both an impressive and unimpressive number, and understanding that is key to understanding the difficulty of the robocar problem.

One of the earliest pioneers, Ernst Dickmanns did a long highway drive 20 years ago, in 1995. He reported the system drove 95% of the time, kicking out every 10km or so. This was a system simply finding the edge of the road, and keeping in the lane by tracking that. Delphi’s car is much more sophisticated, with a very impressive array of sensors — 10 radars, 6 lidars and more, and it has much more sophisticated software.

99% is not 4% better than 95%, it’s 5 times better, because the real number is the fraction of road it could not drive. And from 99%, we need to get something like 10,000 times better — to 99.9999% of the time, to even start talking about a real full-auto robocar.•