Tuesday, 19 January 2016

Self-driving cars, internet balloons, and why Google is radical

Went to a talk about [x] today (formerly: Google[x]).

Unlike other Silicon Valley companies that are "making the world a better place" (according to Silicon Valley, the TV series), [x] is "making the world a radically better place" (according to today's presenter).

If you followed Google I/O this year or gone to some other Google talk about [x] and its moonshots, or if you've just glued yourself to the aforementioned TV series, in all three cases you would have seen the following slide:

At MIT today, we heard about 2 projects from [x] (out of the 10 or so that are currently public): self-driving cars and internet balloons.


A huge problem? The 1.2 million annual traffic accidents, 93% of which are due to human error.
The radical solution: self-driving cars and the required road infrastructure.
Breakthrough tech: software with realtime sensor processing.

The principle on which this whole autonomous car thing hinges, is an initial full laser mapping of the urban area in which the car is to drive (road and buildings and all) with which the car's real-time sensor data is then aligned for accurate positioning and localization within the lane.

Is it feasible to have to pre-map every urban environment, and then to update it when it changes? Ah, well on the one hand Google Streetview cars have already shown us something about feasibility, but in the longterm, the self-driving cars will continue to collect data as they drive, driving and mapping simultaneously.

In fact, already these self-driving cars can send alerts to the other self-driving cars on the road when things look different than expected (lane closures on this street, sending you an updated map...). Such alerts also get sent to the home base for potential updates to the whole system. The cars are connected to each other and their home base via the internet. (Is this information transfer secure? Not to worry, Google knows a thing or two about internet security.)

So, some basic rules are hard-coded, but there's also a lot of machine learning from the many hours spent by these cars on the roads in all sorts of situations. These cars have to learn about the distribution of pedestrian movements (how fast they walk, how quickly they can switch direction, etc.), the typical behaviors of pedestrians, bicyclists, and pedestrians in response to bicyclists. They plot out the trajectories of all vehicles currently on the road and anticipate their next move.

The big challenge? Achieving ridiculous recall and precision. A recall of 99% when pedestrians are involved is not going to do it (you just can't afford to lose a few pedestrians here and there while you tweak your algorithm). Recall is very much about the safety of the pedestrians, but precision is also about the safety of the vehicles (and their passengers). If the car freaks out at every road sign blowing in the wind, not only will the ride be very uncomfortably jerky, but the car might swerve into other cars to avoid hitting the mistakenly classified "pedestrian".

There's other behaviors built-in for the comfort and safety of the passenger: for instance, shifting in the lane (all while avoiding other cars) when passing large trucks. Even if you have everything under control, you don't want your passenger getting antsy about a truck that's too close for comfort.

These cars also slow down at railroads, near parked cars, and while passing bicyclists. Their long-range and short-range sensors ensure the car is very much aware of its surroundings. So much so that the 15 cm resolution of its sensors allows the cars to recognize the fine hand gestures of the traffic controller waving in the middle of the intersection or the bicyclist signaling to change lanes. In making decisions, the cars also make use of all sorts of contextual information: are other cars moving? Why have they stopped? Are there traffic cones around?

And all of this computation and communication is happening on a single CPU. How's that for efficient resource sharing? (but watch out for GPUs coming to a car near you...)

These cars have been designed for zero driver assistance. Are you going to see any sort of control device built into them like a wheel or a break pedal? No chance. This is Google's approach. No need so far: of the 13 driverless car incidents to date, all were the fault of the other drivers. (Side thought: what if the sheer sight of this car on the road is distracting?)

But these cars sure go through a lot of situational testing. And yes, they're good in harsh weather conditions too (confirmed by hardware reliability tests and buckets of water). The QA testing position for the self-driving car project must be damn awesome.


Another huge problem? 2/3 of the world does not have internet.
The radical solution? Balloons!
Breakthrough tech: large-scale dynamic optimization of balloon network.

We're talking global optimization (literally, global). Consider a network of balloons that are distributed around the world that need to follow pre-planned flight paths, adapt to changing wind conditions, and deal with intermittent (sometimes flaky) instructions - all while providing continuity of internet service. This is Project Loon.

Communication with these balloons as they pass over the oceans is through satellite phones. In these conditions, instructions can be dropped, intermittent, or conflicting, and the balloons must nevertheless make good decisions based on limited information and changing wind gusts.

So how does it all work? These balloons fly at an altitude of 20 km - twice as high as airplanes and the weather, so at least a few less problems to deal with. They follow air currents at different altitudes, and steer with vertical motion to end up in an air current moving in the desired direction. An internal balloon pumps air in and out, and with essentially the power of a fan, can move the exterior balloon up and down. Additional power comes from solar cells, but in most cases the wind currents are sufficient to propel the balloons.

A network of balloons thus moves through air currents, one displacing another, to provide continuous, seamless internet service to the cities below. It's kind of like how when you're moving, your service has to remain continuous despite shifting cell towers; but in this case, the city below is stationary, and it is the internet source that is moving above. This is the local optimization part.

Sometimes, balloons also need to be dispatched to the location of a natural disaster, and this has to happen fast. Balloons also need to function in all kinds of harsh conditions, and with local repair most often unavailable, redundancy is key. Redundancy, redundancy, redundancy. Remember how the self-driving cars had 1 CPU? Well these babies have upwards of 40. And if something does goes down, you have to go fetch it... wherever it ends up (can you climb trees and mountains?). Another damn awesome job.

These projects, and all the rest in the [x] repository are driven in part by the slogan: "we need to fail faster". Innovation comes from trying radically new things, and radically new things can often lead to failure. Failing faster means trying again sooner.

I gotta hand it to you, Google sells it well. Another take-away? It seems Google likes the word radical.

1 comment:

  1. it's nice concept . i am looking for this . Finally i find out this .