The cost of the hardware, sensors and mapping is expensive (initially) if you are talking about adding the hardware to a vehicle that one person or family will own. If you are talking about adding it to each vehicle in an autonomous taxi fleet, it suddenly makes a lot more sense and economies of scale kick in quickly.

You are replacing a taxi drivers wages with sensors and mapping essentially. Not to mention extremely-well paid surgeons who clean up after drunk driving accidents, theatre nurses, insurance costs, cost of mechanics.....this technology has so many implications it's hard to visualise them all initially. It's going to be extremely disruptive to some industries, and a boon to others (e.g. where I live, rural pubs are in trouble. This could change all that - "get driven home at 200mph in perfect safety after a night getting smashed!" etc.)

The first to use this technology will be long-haul cargo companies, and it will happen when the technology will be cheaper than having truck drivers employed, and when the safety is good enough.

I imagine a scenario where trucks will drive themselves at night, over the long distances, when few people are around, and when they get close to cities, a real truck driver will jump in and navigate to the end destination, in the day, when there's a lot of people around.

And not until people are used to self-driving trucks will the tech be available for personal transportation, even though the technology will be good enough and increase safety before that.

(In the same way self-flying airplanes will come to cargo transports first, and human transport much later)

I'm doing some work at the moment using Kinects. Obviously, the range and accuracy is much reduced, but for $100, it's not bad for the price. I'd expect that LIDARs can come down a lot in price once they stop being speciality equipment and start being used in more things.

Some people have noted that personal ownership of a self driving car is non-optimal in terms of effective use of this tech. But you make an excellent indirect connection. Since these things would be be most effective with a lot of usage, electrical cars or some other renewable energy source would be ideal for the environment.

Not to speak like a party-pooper, but could someone potentially mess with this LIDAR system by aiming a very bright IR beam at the device, if not a matching laser itself?

Bringing down the cost of the LIDAR will be one major task, the other will be making this untouchable in the environment, IMO.

> Not to speak like a party-pooper, but could someone potentially mess with this LIDAR system by aiming a very bright IR beam at the device, if not a matching laser itself?

You could probably screw up the samples that would be taken from wherever your beam hits on the mirrors. But you could also shine a laser in someones eyes while they're driving and seriously impair them as well.

You _would_ screw it up - but only that mirror. The systems are designed for safety - the car would likely stop.

But what about reflective objects? The roadway is covered with different degrees of weirdly-reflective surfaces. Surely those beams land in places where they shouldn't (on the mirrors), and surely the car doesn't stop every time.

LIDAR works _through_ reflections. It essentially paints out the surrounding area with laser pulses and then measures the time for those pulses to come back. Since lasers travel at the speed of light, this time-to-return is quite small and hence they need quite accurate clocks (which is where a lot of the expense comes in).

That being said, laser range-finding works well on diffuse surfaces; this is because when diffuse surfaces reflect, they reflect the incoming light in a broad hemisphere (or cone) which sends the laser pulse out in many directions. Consequently, the surface to measure can be at a variety of angles and still be picked up by the LIDAR sensors (the sensor doesn't care about strength, only time-to-return)

So in terms of "weirdly reflective" surfaces out there, almost everything is diffuse enough for LIDAR to work well. Car hoods, carbon fiber, chrome wheel covers, etc. The only exception is glass, where generally lasers travel straight through and don't return to the sensors. So LIDAR actually detects "holes" in these situations, as if other cars were driving with no windshield an all their windows down.

So the only real risk would be a large plane of glass in the middle of the highway with completely normal road behind it. LIDAR would miss the glass, and the cameras would not be able to see it either. Most real drivers would fail at that too though :D

I guess what I meant was, surely there are weird surfaces that have multiple bounces. Or light being emitted could bounce between 2 cars and back to the sensor...or off a shop window (at a high angle, fresnel reflection), back onto something else, and back into the sensor. This data would come back into the sensor and it wouldn't be expected.

So surely the automated car, when it sees data it does not expect, does not stop, because it must see data it does not expect often through multiple bounces, right?

Sure, the situation you described certainly happens and is just considered a general noisy measurement. The car could detect empty road one second and all of the sudden some small object _right_ in front of the grill at the next frame. To avoid this "freakout" situation where the car slams on the brakes every time a noisy measurement comes in, all the data is passed through a particle filter (or Kalman filter) first before being processed by the AI.

The transition model of the cars environment is known, so it can reason that "there is a very small chance this reading represents a real object and is not noise, because i did not detect anything near this position over the last 20 frames, so I'm going to assign a very low probability to it." Hence you can clean up the data really well because you're measuring an outdoor environment, not a meteor shower (or anything else where objects could appear and disappear every frame due to high velocity).

What about a fence - the wires reflect the light but they make up perhaps 5% of the crossection - the LIDAR could "see" only the holes

Radar can handle that problem. To be fair, probably not the automative radars they're already using, but something in the tens of GHz range could do it.

This isn't that much of a problem in practice. As long as the surface is partially diffuse, some of the reflected light will make it back to the sensor; light that gets scattered elsewhere is inconsequential. All you have to worry about is completely specular surfaces like mirrors, because just like a camera, the LIDAR won't be able to distinguish the reflections from real objects.

The Lidar is really only a convenient system for the prototype - production units would rely mostly on imaging and limited range radar for parking / collision.

Having said that - there is no real difficulty in making Lidar very cheap if you wanted to - it's all solid state

The Lidar cost is not a concern in my opinion. What is Lidar actually used for right now? I'm guessing just military, science and research. This makes it very expensive. If there were demand for a million of them, they would cost a tenth as much-or less

40 inch flat panels used to cost $40000 in higher volumes (production quantity) than LIDARs are shipping now. Now these panels cost ~$500 (and are better).

Isn't the kinnect an example of very inexpensive LIDAR? (I may be wrong)

No. A Kinect uses structured light projection to create a discernible pattern. A Velodyne uses Time-of-Flight lasers.

The Kinect uses infrared to see in 3D. Here's a cool video showing the infrared light field: http://www.youtube.com/watch?v=6CFoOFZ6ifc

Another problem is that "maps" change quite often due to road construction, detours or whatever.

Correct, but these autonomous vehicles can (should) also forward updates to the maps server. It's the same as with MAC address/WiFi positioning. When you enable that positioning on your smartphone, it sends a list of MAC/WiFi entities with reception quality to the central server. The server looks up where you are based on that data, but at the same time updates its lists: some routers have disappeared (people moved) and some were added. Crowdsourcing at its best.