Driving without a map is another example of being cheap rather than being safe
There was a lot of buzz yesterday about publication by a team at CSAIL and MIT about their research on driving without a map.
Rather than describing a big breakthrough, what is described is fairly similar to the work done in the first two DARPA Grand Challenges, which by 2005 had the winning cars driving a 150 mile course through desert roads with just GPS waypoints. (The CMU teams did try to do some rough mapping in the 4 hour window after getting the waypoints.) Because humans can drive without a map, why can't our robots?
I have discussed the merits of driving without a map before. Any car that can drive without a map is a great resource for building a map -- why would you want to throw away the useful information from another car that drove the road before you, with the addition of the ability to process it with as much CPU as you want, and to see it from different vantage points? It's a huge win.
It is also important to understand that cars that drive with maps still must function when their map is wrong because the world changes underneath the map. It is still more capable with an old, wrong map (whose flaws are, as it turns out, obvious) than if it makes zero use of prior information. Here's a post on this.
But I want to discuss the real flaw in this logic, which I see manifest in several other areas of development.
This is the wrong time to make robocar driving cheaper
The mistake is the natural instinct everybody has to do things at lower cost. If something is expensive, like mapping, bandwidth or LIDARs, we all automatically want to think of how we might eliminate it, or make it cheaper. That's a good instinct, and in a number of years, that will be an important thing to do. Today, if there is a choice between being cheap and being safe sooner, then being safe is generally the way to go.
For all the teams trying to win the robocar market, getting to market is paramount, and that means getting to the level of safety necessary to go to market, and operating at that level of safety. The early prototypes and pilot projects should not decide to make their vehicle less safe just to save money. Almost any amount of money.
That's why you've seen most cars using a $75,000 Velodyne LIDAR. That cost is too high, though everybody knows that by production time, much cheaper LIDARs will be available (they already are.) But some think even $5,000 LIDARs are too expensive, and hope to drive with just cameras. I say it would be crazy to give up any truly useful sensing ability just to save $5,000 in the first five years of operation.
The logic is different for taxi fleets and cars-for-sale. Car makers are used to living in a world where adding $5,000 to the parts cost for a car adds $10,000 or more to the retail price and puts it in an entirely different car class. They have the intuition to be cheap pushed into them hard. For a taxi fleet, an extra $5K per taxi is not a big deal in the early years, where the issue is getting out there and getting customers, not being the cheapest. In the 250,000 mile life of a taxi, it's 2 cents extra per mile. When you are taking taxi rides down from $2/mile to under $1, eliminating the 30-90 cent/mile driver, that's not a dealbreaker.
Once there is a large market, with many competitors, then you can start competing on price. Then the ability to be a few cents cheaper will make the difference. In the early days, being safer, sooner is what will make the difference.
The article cited above makes another strange mistake. It believes that the size of maps is a big cost issue.
“Maps for even a small city tend to be gigabytes; to scale to the whole country, you’d need incredibly high-speed connections and massive servers,” says Teddy Ort of CSAIL.
Oh no! Gigabytes! If you can even get a drive that is that small. Cars will easily be able to store the base maps of 99.9% of the places they are going to go, updating them from time to time when connected by wifi or similar. If a car needs to drive a road entirely outside its predicted range, it only needs to download just those streets as it gets close to them, and updates for any streets along its specific route. The data needs are quite modest, easily handled with 3G networks, let alone 5G.
If a car should somehow need to transfer terabytes of data, it has an ability that ordinary computers don't -- it's a robot, and can drive when empty to a location where it can do that, even if it doesn't have wifi where it otherwise parks. It can even drive to a drive swap station if truly desired. Amusingly, if you want to send a petabyte across town, it might be the cheapest and fastest way to send it would be with a robocar carrying a box of drives.
The same error is made by MobilEye with their REM mapping plan. With REM, they have proudly announced that they have made map data and updates from cars super-small, so they can be constantly updated over mobile networks. That's not a bad thing per se, but betting that bandwidth is going to be expensive has rarely been the right bet. Obviously, if you can use smaller files with zero cost in capability or safety, do it. But there's usually a tradeoff.
Mapping is expensive. In particular, since the AI needed to fully understand the road is not yet ready, most teams want human review of the maps generated by their AIs. The actual driving of the roads to gather the data is expensive if you pay people to do it, but once you have a large enough fleet of cars out there, I don't think the cost will be a major burden. Some day -- but not on day one -- an ability to do the rarely used roads that are uneconomical to map will be very worthwhile. But remember, those roads are the uneconomical ones, so demand is inherently low, and the people who want to drive them will be willing to manually drive them once for a mapping pass.
As noted, the most common instance of this error has been the effort by Tesla and a few other teams to work on cars that don't use LIDAR. This is a risky bet. To drive without LIDAR requires a real computer vision breakthrough. That breakthrough will come, but nobody knows when, and even when it does come, I still believe that the breakthrough camera plus LIDAR will still be a bit better. Nobody who uses LIDAR is ignoring the computer vision. If you win the bet, you may be a bit cheaper. That might help you if you are Tesla, selling cars to end-users. But it's a risky bet, because if you lose, you are way behind.