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Google Research Leads To Automated Real-Time Pedestrian Detection
An anonymous reader writes with a link to a story about one of the unexciting but vital bits of technology that will need to be even further developed as autonomous cars' presence grows: making sure that those cars don't hit people. Google researchers have recently presented findings about a method that tops previous ones for real-time pedestrian detection using neural nets "that is both extremely fast and extremely accurate." From the article: There are other approaches that provide a real-time solution on the GPU but in doing so, have not achieved accuracy targets (in this real-time approach there was a miss rate of 42% on the Caltech pedestrian detection benchmark). Another approach called the VeryFast method can run at 100 frames per second (compared to the Google team's 15) but the miss rate is even greater. Others that emphasize accuracy, even with GPU acceleration, are up to 195 times slower.
But why are they implying that something is almost done, when quite a few basic problems haven't still been tackled?
This isn't about not hitting pedestrians in the roadway. This is about categorizing objects outside the roadway so that you know if they are pedestrians, who may enter the roadway at any time, or stationary objects, which may be presumed to stay stationary. Once categorized as a pedestrian, additional algorithms can be used to guess whether the pedestrians are going to enter the roadway and take defensive driving precautions to avoid hitting them.
Note that even if this algorithm were wrong 100% of the time, the actual injury rate would be much lower. While pedestrians can enter the roadway at any time, most stick to the sidewalk most of the time. Even those who enter the roadway will generally be trying to avoid being hit by cars. So normal obstacle detection (which doesn't care if they are a pedestrian or an inanimate object) will be able to avoid them most of the time. Which may help explain why Google cars haven't been hitting 26% of the pedestrians they pass but instead 0%.
In other words, this isn't quite as basic of a problem as you make it out to be. This will make no difference in the vast majority of trips. In a small number of trips, it may cause the car to take extra precautions to avoid potential interactions with a pedestrian. Even there, most of the time it won't matter (i.e. the vehicle would not hit the pedestrian even without the precautions). In a very, very small number of trips, this could allow an autonomous car to avoid hitting a pedestrian that a human driver would have hit. In a truly miniscule number of trips, this could allow the autonomous vehicle to avoid hitting a pedestrian that a human driver would have avoided but the autonomous vehicle otherwise would not have.
Even without this, autonomous cars are likely to be safer than human drivers. The biggest contributor to accidents by human drivers is negligence. The driver makes a mistake, is distracted, or whatever. Those things don't happen to AI. I'm not saying that we shouldn't tweak the AI to avoid a one-in-a-million accident. We should. But it's not an absolute bar to deployment. Presumably we will continue to tweak and improve the AI after deployment.
Actually, the biggest problem that I see with this is that they are using neural nets to "learn" the difference between pedestrians and other objects. It's much harder to fix a neural net problem than an algorithmic problem. You can tweak an algorithm. With a neural net, changes are as likely to make the situation worse as better. This means that we don't fix the system any more than we would fix a human driver who was in an accident. Neural nets are just too complicated to tweak -- that's why we're using a neural net rather than an algorithmic solution in the first place.