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To fix gerrymandering, a test is needed -- or an interstate compact

One of the key flaws in the US political system is gerrymandering. I have written about this before even proposing my own method of redistricting, but such proposals only have a limited utility.

In this article I present why court solutions have had trouble, and a potentially new approach using an interstate compact.

Gerrymandering is particularly bad in the USA, but it’s a general “bug” in many democratic systems. The flaw is often summed up with the phrase “The politicians pick the voters instead of vice versa.” When the incumbent legislators and parties can draw the districts, they can bias the system heavily in their favour. In the USA, the house of representatives is currently highly biased towards the Republican party. It is often cited that the Republicans won 49.9% of popular votes for congress but got 55% of the seats. You can’t actually add the individual house votes, because people vote (or rather stay away) differently in safe districts than they do in contested one, but the margin is large enough that the trend is clear.

This is in large part due to Operation Redmap which is documented in the book Ratfucked. It truly fits the description “fiendishly clever plan” and exploits the bug to the level of making it close to permanent.

How districts are drawn is left to the states both in the constitution and the law. Some states have moved to create more fair districting rules, the sort of rules you would make up if you were doing it from a nonpartisan standpoint. However, the hard fact is that those states which do this are chumps. It does not make the system more fair if one side stops cheating — and I do think of gerrymandering as cheating — and the other side keeps on cheating. It just assures victory for the cheating side going forward. At the same time, having all sides cheat indefinitely is not a good solution either.

The constitution says very little about districting. In fact, it doesn’t even demand districts! States could have, if they chose, selected their representatives in a statewide proportional vote. Later federal laws, however, have demanded each person have one congress member, which demands geographic districts. About half the states require the districts be contiguous, but the others don’t. The voting rights act and other principles have forbidden drawing the lines on racial or minority grounds, but not on the grounds of “this helps incumbents keep their seats” — that’s still largely within the rules.

In any event, as long as gerrymandering is benefiting the GOP, they are not going to commit political suicide to remove it. States controlled strongly by one party or the other will resist willfully hurting their own parties, though there are exceptions when states have ballot resolutions. The supreme court ruled, barely, that the public can supersede the legislatures on this matter with a ballot proposition, and so that has happened. While the public belong to parties, they are actually more interested in fairness than party loyalty.

A constitutional amendment could fix this, but that’s not going to happen. And strong federal law could probably fix it, but that’s not coming from houses controlled by the people which benefit from the cheating.

As such, the solution can only come from the courts, or ballot propositions in a balanced set of states.

A good summary of the rules around districting in the different states can be found at this site.

But it’s not actually fair play, say the courts

Justices of the supreme court have reportedly all denounced gerrymandering to cement political control. They agree that it violates the principles of the constitution of one person one vote and equal protection, as it effectively eliminates for partisan reasons the voting power of many. Even agreeing with this, for now they feel powerless to stop it.

We can all see gerrymandering happen, but for the courts to do something about it, they would need to define fair and unbiased test which says when it is happening. This is hard, as courts are reluctant to write sets of rules like that — that is the province of the other branches of government. Courts don’t make the rules, they just decide if people are playing fairly by the rules that the other branches created.

So while it’s easy for you or I to propose fair rules for districting — rectangular districts or my own convexity test above — these just aren’t the sort of rules courts are willing to make up. You can’t extract them from the constitution. A court can look at a crazily shaped district and know “this is unfair” but it has to come up with a way that the states can objectively know what is fair and what isn’t, without being the author of its own rules.

One proposed rule that’s been advocated is the voting efficiency gap. Here, they try to measure how many votes were “wasted” because of district design. If a district went 80% for one party and 20% for the other, 30% of party A’s votes are wasted, and20% of party Bs, and the difference between these numbers tells how biased that election was.

It’s a nice test but one can see immediate flaws. For example, in a state biased 55% to 45%, a “perfect” districting where every district has the same balance as the state would result in 100% of seats for the dominant party. Since one party is strong in cities and the other strong in the country, any geographic set of districts is going to have these “inefficiencies” with inner cities voting 80% Democratic in the same state as a rural district votes 80% Republican — without any intent to cheat in how the lines are drawn. As noted, proportional non-geographic districts are not going to happen.

The courts, if they are to help us, need a test which will clearly let them tell states, “If you don’t draw your districts to match this test, they will be ruled invalid.” It’s easy to come up with fair, non-partisan tests to use, but the problem is that it is easy and so there are several you could use — and why should one be chosen over another? The legislatures can choose one option from many, but the courts are not to be arbitrary in that way. Their test has to clearly match some principle they find in the law.

You can propose convexity, or straight lines, or random selection — but none of them answer the question of “why does the law demand that particular one, vs. another?” They will ask this because any system, even if non-partisan, will benefit one party more than a different choice and thus have the appearance of being chosen from the pool for a partisan reason. And perhaps more than the appearance.

Ballot propositions and a State Compact

Individual states deciding to play fair just cede their power. Perhaps another option is possible — through a compact of states dedicated to fair districting.  read more »

Flying cars, electogliding and noise

The recently released national noise map makes it strikingly clear just how much air travel contributes to the noise pollution in our lives. In my previous discussion of flying cars I expressed the feeling that the noise of flying cars is one of their greatest challenges. While we would all love a flying car (really a VTOL helicopter) that takes off from our back yards, we will not tolerate our neighbour having one if there is regular buzzing and distraction overhead and in the next yard.

Helicopters are also not energy efficient, so real efforts for flying cars are fixed wing, using electric multirotors to provide vertical take-off but converting in some way to fixed wing flight, usually powered by those same motors in a different orientation. If batteries continue their path of getting cheaper, and more importantly lighter, this is possible.

Fixed wing planes can be decently efficient — particularly when they travel as the crow flies — though they can have trouble competing with lightweight electric ground vehicles. Almost all aircraft today fly much faster than their optimum efficiency speed. There are a lot of reasons for this. One is the fact that maintenance is charged by the hour, not the mile. Another is that planes need powerful engines to take off, and people are in a hurry and want to use that powerful engine to fly fast once they get up there.

Typical powered planes have a glide ratio (which is a good measure of their aerodynamic efficiency) around 10:1 to 14:1. That means for every foot they drop, they go forward 10 to 14 feet. Gliders, more properly known as “sailplanes” are commonly at a 50:1 glide ratio today and go even higher. Sailplane pilots can use that efficiency to enter slowly rising columns of air found over hot spots on the ground and “soar” around in a circle to gain altitude, staying up for hours. Silent flying is great fun, though the tight turns to rise in a thermal can cause nausea. Efficient sailplanes are also light and can have fairly bumpy rides. (Note as well that the extra weight of energy storage and motors and drag of propellers means a lower glide ratio.)

It is the silent flight that is interesting. An autonomous high efficiency aircraft, equipped with redundant electric motors and power systems, need not run its engines a lot of the time. While you would never want to be constantly starting and stopping piston powered aircraft engines, electric engines can start and stop and change speed very quickly. The motors provide tremendous torque for fast response times. It would be insane to regularly land your piston powered aircraft without power, figuring you can just turn on the engine “if you need it.” It might not be that crazy to do it in an electric aircraft when you can get the engine up and operating in a fraction of a second with high reliability, and you have multiple systems, so even the rare failures can be tolerated.

Both passengers and people on the ground would greatly appreciate planes that were silent most of the time, including when landing at short airstrips. It could make the difference for acceptance.

For a more radical idea, consider my more futuristic proposal of airports that grab and stop planes with robotic platforms on cables. Such a system would even allow for mostly silent takeoff in electric aircraft.

Making efficient aircraft VTOL is a challenge. They tend to have large wingspans and are not so suitable for backyards, even if they can hover. But the option for redundant multirotor systems makes possible something else — aircraft wings that unfold in the air. There are “flying cars” with folding wings which fold the wings up so the car can get on the road, but unfolding in the air is one of those things that is insane for today’s aircraft designs. A VTOL multirotor could rise up, unfold its wings, and if they don’t unfold properly, it can descend (noisily) on the VTOL system, either to where it took off form, or a nearby large area if the wings unfolded but not perfectly. An in-flight failure of the folding system could again be saved (uncomfortably but safely) by the VTOL system.

We don’t yet know how to make powered vertical takeoff or landing quiet enough. We might make the rest of flight fairly silent, and make the noisy part fairly brief. The neighbours don’t all run their leaf blower several times per day. But a combination of robocars that take you on the first and last kilometer to places where aircraft can make noise without annoyance if they do it briefly might be a practical alternative.

Planes that fly silently would not fit well with today’s air traffic control regiments that allocate ranges of altitude to planes. A plane with a 50:1 ratio could travel 10 miles while losing 1,000 feet of altitude, then climb back up on power for another silent pass. But constant changing of altitude would freak out ATC. A computerized ATC for autonomous planes could enable entirely different regimens of keeping planes apart that would allow this, and it would also allow long slow glides all the way to the runway.

LIDAR (lasers) and cameras together -- but which is more important?

Recently we’ve seen a series of startups arise hoping to make robocars with just computer vision, along with radar. That includes recently unstealthed AutoX, the off-again, on-again efforts of and at the non-startup end, the dedication of Tesla to not use LIDAR because it wants to sell cars today, before LIDARs can be bought at automotive quantities and prices.

Their optimism is based on the huge progress being made in the use of machine learning, most notably convolutional neural networks, at solving the problems of computer vision. Milestones are dropping quickly in AI and particularly pattern matching and computer vision. (The CNNs can also be applied to radar and LIDAR data.)

There are reasons pushing some teams this way. First of all, the big boys, including Google, already have made tons of progress with LIDAR. There right niche for a startup can be the place that the big boys are ignoring. It might not work, but if it does, the payoff is huge. I fully understand the VCs investing in companies of this sort, that’s how VCs work. There is also the cost, and for Tesla and some others, the non-availability of LIDAR. The highest capability LIDARs today come from Velodyne, but they are expensive and in short supply — they can’t make them to keep up with the demand just from research teams!

Note, for more detailed analysis on this, read my article on cameras vs. lasers.

For the three key technologies, these trends seem assured:

  1. LIDAR will improve price/performance, eventually costing just hundreds of dollars for high resolution units, and less for low-res units.
  2. Computer vision will improve until it reaches the needed levels of reliability, and the high-end processors for it will drop in cost and electrical power requirements.
  3. Radar will drop in cost to tens of dollars, and software to analyse radar returns will improve

In addition, there are some more speculative technologies whose trends are harder to predict, such as long-range LWIR LIDAR, new types of radar, and even a claimed lidar alternative that treats the photons like radio waves.

These trends are very likely. As a result, the likely winner continues to be a combination of all these technologies, and the question becomes which combination.

LIDAR’s problem is that it’s low resolution, medium in range and expensive today. Computer Vision (CV)’s problem is that it’s insufficiently reliable, depends on external lighting and needs expensive computers today. Radar’s problem is super low resolution.

Option one — high-end LIDAR with computer vision assist

High end LIDARs, like the 32 and 64 laser units favoured by the vast majority of teams, are extremely reliable at detecting potential obstacles on the road. They never fail (within their range) to differentiate something on the road from the background. But they often can’t tell you just what it is, especially at a distance. It won’t know a car from a pickup truck, or 2 pedestrians from 3. It won’t read facial expressions or body language. It can read signs but only when they are close. It can’t see colours, such as traffic signals.

The fusion of the depth map of LIDAR with the scene understanding of neural net based vision systems is powerful. The LIDAR can pull the pedestrian image away from the background, and then make it much easier for the computer vision to reliably figure out what it is. The CV is not 100% reliable, but it doesn’t have to be. Instead, it can ideally just improve the result. LIDAR alone is good enough if you take the very simple approach of “If there’s something in the way, don’t hit it.” But that’s a pretty primitive result that make brake too much for things you should not brake for.

Consider a bird on the road, or a blowing trash bag. It’s a lot harder for the LIDAR system to reliably identify those things. On the other hand, the visions systems will do a very good job at recognizing the birds. A vision system that makes errors 1 time every 10,000 is not adequate for driving. That’s too high an error rate as you encounter thousands of obstacles every hour. But missing 1 bird out of 10,000 means that you brake unnecessarily for a bird perhaps once every year or two, which is quite acceptable.

Option two — lower end LIDAR with more dependence on vision

Low end lidars, with just 4 or so scanning planes, cost a lot less. Today’s LIDAR designs basically need to have an independent laser, lens and sensor for each plane, and so the more planes, the more cost. But that’s not enough to identify a lot of objects, and will be pretty deficient on things low to the ground or high up, or very small objects.

The interesting question is, can the flaws of current computer vision systems be made up for by a lower-end, lower cost LIDAR. Those flaws, of course, include not always discerning things in their field. They also include needing illumination at night. This is a particular issue when you want a 360 degree view — one can project headlights forward and see as far as they see, but you can’t project headlights backward or to the side without distracting drivers.

It’s possible one could use infrared headlights in the other directions (or forward for that matter.) After all, the LIDAR sends out infrared laser beams. There are eye safety limits (your iris does not contract and you don’t blink to IR light) but the heat output is also not very high.

Once again, the low end lidar will eliminate most of the highly feared false negatives (when the sensor doesn’t see something that’s there) but may generate more false positives (ghosts that make the vehicle brake for nothing.) False negatives are almost entirely unacceptable. False positives can be tolerated but if there are too many, the system does not satisfy the customer.

This option is cheaper but still demands computer vision even better than we have today. But not much better, which makes it interesting.

Other options

Tesla has said they are researching what they can do with radar to supplement cameras. Radar is good for obstacles in front of you, especially moving ones. Better radar is coming that does better with stationary objects and pulls out more resolution. Advanced tricks (including with neural networks) can look at radar signals over time to identify things like walking pedestrians.

Radar sees cars very well (especially licence plates) but is not great on pedestrians. On the other hand, for close objects like pedestrians, stereo vision can help the computer vision systems a lot. You mostly need long range for higher speeds, such as the highways, where vehicles are your only concern.

Who wins?

Cost will eventually be a driver of robocar choices, but not today. Today, safety is the only driver. Get it safe, before your competitors do, at almost any cost. Later make it cheap. That’s why most teams have chosen the use of higher end LIDAR and are supplementing in with vision.

There is an easy mistake to make, though, and sometimes the press and perhaps some teams are making it. It’s “easy” on the grand scale to make a car that can do basic driving and have a nice demo. You can do it with just LIDAR or just vision. The hard part is the last 1%, which takes 99% of the time, if not more. Google had a car drive 1,000 miles of different roads and 100,000 total roads in the first 2 years of their project back in 2010, and even in 2017 with by far the largest and most skilled team, they do not feel their car is ready. It gets easier every day, as tech advances, to get the demo working, but that should not be mistaken for the real success that is required.

California New Regs, Intel buys MobilEye, Waymo sues Uber

California has published updated draft regulations for robocars whose most notable new feature is rules for testing and operating unmanned cars, including cars which have no steering wheel, such as Google, Navya, Zoox and others have designed.

This is a big step forward from earlier plans which would have banned testing and deploying those vehicles. That that they are ready to deploy, but once you ban something it’s harder to un-ban it.

One type of vehicle whose coverage is unclear are small unmanned delivery robots, like we’re working on at Starship. Small, light, low speed, inherently unmanned and running mostly on the sidewalks they are not at all a fit for these regulations and presumably would not be covered by them — that should be made more explicit.

Another large part of the regulations cover revoking permits and the bureaucracy around that. You can bet that this is because of the dust-up between the DMV and Uber/Otto a few months ago, where Uber declared that they didn’t need permits (probably technically true) but the DMV found it not at all in the spirit of the rules and revoked the licence plates on the cars. The DMV wants to be ready to fight those who challenge its authority.

Intel buys MobilEye

Intel has paid over $15B to buy Jerusalem based MobilEye. MobilEye builds ASIC-based camera/computer vision systems to do ADAS and has been steadily enhancing them to work as a self-driving sensor. They’ve done so well the stock market already got very excited and pushed them up to near this rich valuation — the stock traded at close to this for a while, but fell after ME said it would no longer sell their chips to Tesla. (Tesla’s first autopilot depended heavily on the MobilEye, and while ME’s contract with Tesla explicitly stated it did not detect things like cross-traffic, that failure to detect played a role in the famous Tesla autopilot fatal crash.

In a surprising and wise move, Intel is going to move its other self-driving efforts to Israel and let MobilEye run them, rather than gobble them up and swallow/destroy them. ME is a smart company, fairly nimble, though it has too much focus on making low-cost sensors in a world where safety at high cost is better than less safety at low cost. (Disclaimer: I own some MBLY and made a nice profit on it in this sale.)

MobilEye has been the leader in doing ADAS functions with just cameras and cameras+radar. Several other startups are attempting this, and of course so is Tesla in their independent effort. However, LIDAR continues to get cheaper (with many companies, including Quanergy, whom I advise, working hard on that.) The question may be shifting from will it be cameras or lasers? to “will it be fancy vision systems with low-end LIDAR, or will it be high-end LIDAR with more limited vision systems?” In fact, that question deserves another post.

Waymo and Uber Lawsuit

I am not going to comment a great deal on this lawsuit, because I am close with both sides, and have NDAs with both Otto and formerly with Google/Waymo. There are lots of press reports on the lawsuit, filed by Waymo accusing Anthony Levandowski (who co-founded Otto and helped found the car team at Google) of stealing a vast trove of Google’s documents and designs. This fairly detailed Bloomberg report has a lot of information, including reports that at an internal meeting, Anthony told his colleagues that any downloading he did was simply to allow work from home.

The size of the lawsuit is staggering. Since Otto sold for 1% of Uber stock (worth over $750M) the dollar values are huge, particularly if, as Google alleges, they can demonstrate Uber encouraged wrongdoing. At the same time, if Google doesn’t prove their allegations, Otto and Anthony could file for what might be the largest libel lawsuit in history, since Google published their accusations not just in court filings, but in their blog.

One reason that might not happen is that Uber is seeking to force arbitration. Like almost all contracts these days, the contracts here included clauses forcing disputes to go to arbitrators, not courts. That will mean that the resolution and other data remain secret.

It’s very serious for both sides. Some have said it’s mission critical for Uber, though I have disputed that, pointing out that even if Uber fails to develop good self-drive technology, they remain free to buy it from other people. That’s something the other players can’t do — even Lyft which has bound itself up with GM for now.

At the same time, Uber should fear something else. Uber is nothing, a $0 company, without iPhone and Android. (There is a Windows mobile app but it’s very low penetration.) Uber could push all drivers to iPhone, but if they ever found themselves unable to use Android for customers, they would lose more than they can afford.

I am not suggesting Google would go as far as to pull or block the Uber app on Android if it got into a battle. Aside from being unethical that might well violate antitrust regulations. But don’t underestimate the risk of betting half your business on a platform controlled by a company you go to war with. There are tricks I can think of (but am not yet publishing here) which Google could do which would not be seen as unfair or anti-competitive but which could potentially ruin Uber. Uber and Google will both have to be cautious in any serious battle.

In other Uber news, leaked reports say their intervention rate is still quite high. Intervention figures can be hard to interpret. Drivers are told to intervene at the smell of trouble, so the rate of grabbing the wheel can be much higher than the rate of actual problems. These leaks suggest, however, a fairly high rate of actual problems. This should remind people that while it’s pretty easy for a skilled team to get a car on the road and doing basic driving in a short time, there is a reason that Google’s very smart team has been at it 9 years and is still not ready to ship. The last 1% of the work takes 99% of the time.