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How do self-driving cars know their way around without a map?
Specific self-driving car systems are now being developed for urban and rural settings.
Self-driving cars are coming down the pike, and there’s a lot of excitement and fear amongst the general public about it. Experts say you should be seeing them on the road here and there by 2020. They’ll be the majority of the vehicles out there by 2040. Consider that 90% of all traffic fatalities are due to human error, according to the US National Highway Traffic Safety Administration. But autonomous vehicles are not without controversy.
In March of this year, a woman in Arizona was struck and killed by one of Uber’s self-driving cars, while she was crossing the street. Most experts say that this incident is an anomaly. Nidhi Kalra—a roboticist at the Rand Corporation told Wired that the development of this technology is moving incredibly fast, particularly the software component. “With software updates,” he said, “there’s a new vehicle every week.”
This brings up an interesting question: how do self-driving cars navigate? An important thing to note is that there are many, many companies breaking into the market. Apple, Google, Tesla, Uber, Ford, GM, and more. They each have their own systems, although most work more or less the same.
Big, big data
In a sense, advances in the self-driving vehicle industry are about dealing with huge amounts of data. The hardware in self-driving cars generates tons of it since it’s vital to know exactly where a vehicle is and what’s around it for safety.
Sensors in a vehicle may include:
- LiDAR, for “light detection and ranging” — that bounces anywhere from 16 to 128 laser beams off approaching objects to assess their distance and hard/soft characteristics and generate a point cloud of the environment.
- GPS — that locates the car’s location in the physical world within a range of one inch, at least in theory.
- IMU, for “inertial measurement unit,” — that tracks a vehicle’s attitude, velocity, and position.
- Radar — that detects other objects and vehicles.
- Camera — that captures the environment visually. The analysis of everything a camera sees requires a powerful computer, so work is being done to reduce this workload by directing its attention only to the relevant objects in view.
The challenge is taking in all this information, blending it, and processing it fast enough to be able to make split-second decisions, like whether or not to duck into another lane when an accident seems imminent.
Because all of this equipment generates so much data, and because it’s so expensive — a full sensor rig can easily cost upward of $100k per vehicle — maps for self-driving cars depend on specially equipped mapping vehicles. The maps they produce — actually not maps as we know them, but complicated datasets made up of coordinates — are ultimately loaded into consumer cars that navigate by continually using their own sensor array to compare the map to the actual surrounding environment, and instruct the car where to safely go.
The mapping problem
Obviously, high-quality, accurate and up-to-date maps for these cars are a critical piece of the puzzle. But producing them is hard. Most companies developing maps for self-driving vehicles currently use a system that works fine for research and development but is probably prohibitively expensive and time-consuming for mass production.
The typical strategy
In each car must be, of course, the full array of sensors. In addition, just managing all of that data requires a powerful, desktop-or-better-grade processor and lots of storage space on a hard drive, usually in the car’s trunk. How big? A map of San Francisco alone requires 4 terabytes.
The process for turning all that data into a map, called a “base map,” for a passenger car to use involves driving to a data center, carrying the drive inside — or shipping the drive — getting the data off it, processing the data, and returning the drive to the car. There are three big issues with this:
- The process takes so long that the critical need to keep base maps current is difficult if not impossible to meet.
- Cars can only drive within the areas for which they have base maps, so improvising a destination on the fly is impossible — new base maps are much too large to upload or download while on the road.
- The hardware and labor involved are too expensive to multiply by millions of cars.
One company, Civil Maps, has developed what may be a more realistic solution to the mapping problem. The software in their mapping vehicles analyzes the driving environment within the car, extracting the relevant details via machine learning, and generating what the company calls a “Fingerprint Base Map™” (FBM) that can reduce, for example, that 400 TB San Francisco map to 400 MB, about the size of an MP3 song, which makes sense, since it uses technology similar to what Shazam uses for retrieving songs. The system is nonetheless precise, tracking the vehicle’s location to within 10 centimeters and in what’s called “six degrees of freedom”: the car’s location, altitude, and its attitude relative to the road.
The small size of the FBM means that an area’s base map can be downloaded as needed, even over current cellular networks, so drivers are freed to go wherever they want. (Civil Maps says they can easily fit a whole continent's worth of maps into a car.) Current conditions are uploaded to the company’s cloud, and crowdsourcing produces a continually updated base map. The solution is also much less expensive, with less required storage space and allowing the use of a much less expensive onboard computer, in part because the fingerprints eliminate the need to analyze the camera’s entire view feed, allowing it to recognize and pay attention to just what matters. ￼
Hitting the road
Getting a handle on the special maps that self-driving cars need to avoid either having to carry around a super-computer within each vehicle or crashing into things is a major roadblock the industry is struggling with now. Smart-enough AI within a car to avoid accidents is obviously another key piece of the puzzle.
That’s self-driving cars as they stand now. With such rapid advancements being reported all the time though, one wonders what capabilities future iterations might possess.
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From "if-by-whiskey" to the McNamara fallacy, being able to spot logical missteps is an invaluable skill.
- A fallacy is the use of invalid or faulty reasoning in an argument.
- There are two broad types of logical fallacies: formal and informal.
- A formal fallacy describes a flaw in the construction of a deductive argument, while an informal fallacy describes an error in reasoning.
Appeal to privacy<p>When someone behaves in a way that negatively affects (or could affect) others, but then gets upset when others criticize their behavior, they're likely engaging in the appeal to privacy — or "mind your own business" — fallacy. Examples:<br></p><ul><li>Someone who speeds excessively on the highway, considering his driving to be his own business.</li><li>Someone who doesn't see a reason to bathe or wear deodorant, but then boards a packed 10-hour flight.</li></ul><p>Language to watch out for: "You're not the boss of me." "Worry about yourself."</p>
Sunk cost fallacy<p>When someone argues for continuing a course of action despite evidence showing it's a mistake, it's often a sunk cost fallacy. The flawed logic here is something like: "We've already invested so much in this plan, we can't give up now." Examples:<br></p><ul><li>Someone who intentionally overeats at an all-you-can-eat buffet just to get their "money's worth"</li><li>A scientist who won't admit his theory is incorrect because it would be too painful or costly</li></ul><p>Language to watch out for: "We must stay the course." "I've already invested so much...." "We've always done it this way, so we'll keep doing it this way."</p>
If-by-whiskey<p>This fallacy is named after a speech given in 1952 by <a href="https://en.wikipedia.org/wiki/Noah_S._Sweat" target="_blank">Noah S. "Soggy" Sweat, Jr.</a>, a state representative for <a href="https://en.wikipedia.org/wiki/Mississippi" target="_blank">Mississippi</a>, on the subject of whether the state should legalize alcohol. Sweat's argument on prohibition was (to paraphrase):<br></p><p><em>If, by whiskey, you mean the devil's brew that causes so many problems in society, then I'm against it. But if whiskey means the oil of conversation, the philosopher's wine, "</em><em>the stimulating drink that puts the spring in the old gentleman's step on a frosty, crispy morning;" then I am certainly for it.</em></p>
Slippery slope<p>This fallacy involves arguing against a position because you think choosing it would start a chain reaction of bad things, even though there's little evidence to support your claim. Example:<br></p><ul><li>"We can't allow abortion because then society will lose its general respect for life, and it'll become harder to punish people for committing violent acts like murder."</li><li>"We can't legalize gay marriage. If we do, what's next? Allowing people to marry cats and dogs?" (Some people actually made this <a href="https://www.daytondailynews.com/news/national/cats-marrying-dogs-and-five-other-things-same-sex-marriage-won-mean/dLV9jKqkJOWUFZrSBETWkK/" target="_blank">argument</a> before same-sex marriage was legalized in the U.S.)</li></ul><p>Of course, sometimes decisions <em>do </em>start a chain reaction, which could be bad. The slippery slope device only becomes a fallacy when there's no evidence to suggest that chain reaction would actually occur.</p><p>Language to watch out for: "If we do that, then what's next?"</p>
"There is no alternative"<p><span style="background-color: initial;">A modification of the </span><a href="https://en.wikipedia.org/wiki/False_dilemma" target="_blank" style="background-color: initial;">false dilemma</a><span style="background-color: initial;">, this fallacy (often abbreviated to TINA) argues for a specific position because there are no realistic alternatives. Former British Prime Minister Margaret Thatcher used this exact line as a slogan to defend capitalism, and it's still used today to that same end: Sure, capitalism has its problems, but we've seen the horrors that occur when we try anything else, so there is no alternative.</span><br></p><p>Language to watch out for: "If I had a magic wand…" "What <em>else</em> are we going to do?!"</p>
Ad hoc arguments<p>An ad hoc argument isn't really a logical fallacy, but it is a fallacious rhetorical strategy that's common and often hard to spot. It occurs when someone's claim is threatened with counterevidence, so they come up with a rationale to dismiss the counterevidence, hoping to protect their original claim. Ad hoc claims aren't designed to be generalizable. Instead, they're typically invented in the moment. <a href="https://rationalwiki.org/wiki/Ad_hoc" target="_blank">RationalWiki</a> provides an example:<br></p><p style="margin-left: 20px;">Alice: "It is clearly said in the Bible that the Ark was 450 feet long, 75 feet wide and 45 feet high."</p><p style="margin-left: 20px;">Bob: "A purely wooden vessel of that size could not be constructed; the largest real wooden vessels were Chinese treasure ships which required iron hoops to build their keels. Even the <em>Wyoming</em> which was built in 1909 and had iron braces had problems with her hull flexing and opening up and needed constant mechanical pumping to stop her hold flooding."</p><p style="margin-left: 20px;">Alice: "It's possible that God intervened and allowed the Ark to float, and since we don't know what gopher wood is, it is possible that it is a much stronger form of wood than any that comes from a modern tree."</p>
Snow job<p><span style="background-color: initial;">This fallacy occurs when someone doesn't really have a strong argument, so they just throw a bunch of irrelevant facts, numbers, anecdotes and other information at the audience to confuse the issue, making it harder to refute the original claim. Example:</span><br></p><ul><li>A tobacco company spokesperson who is confronted about the health risks of smoking, but then proceeds to show graph after graph depicting many of the other ways people develop cancer, and how cancer metastasizes in the body, etc.</li></ul><p>Watch out for long-winded, data-heavy arguments that seem confusing by design.</p>
McNamara fallacy<p>Named after <a href="https://en.wikipedia.org/wiki/Robert_McNamara" target="_blank">Robert McNamara</a>, the <a href="https://en.wikipedia.org/wiki/United_States_Secretary_of_Defense" target="_blank">U.S. secretary of defense</a> from 1961 to 1968, this fallacy occurs when decisions are made based solely on <em>quantitative metrics or observations,</em> ignoring other factors. It stems from the Vietnam War, in which McNamara sought to develop a formula to measure progress in the war. He decided on bodycount. But this "objective" formula didn't account for other important factors, such as the possibility that the Vietnamese people would never surrender.<br></p><p>You could also imagine this fallacy playing out in a medical situation. Imagine a terminal cancer patient has a tumor, and a certain procedure helps to reduce the size of the tumor, but also causes a lot of pain. Ignoring quality of life would be an example of the McNamara fallacy.</p><p>Language to watch out for: "You can't measure that, so it's not important."</p>
A new study looks at what would happen to human language on a long journey to other star systems.
- A new study proposes that language could change dramatically on long space voyages.
- Spacefaring people might lose the ability to understand the people of Earth.
- This scenario is of particular concern for potential "generation ships".
Generation Ships<span style="display:block;position:relative;padding-top:56.25%;" class="rm-shortcode" data-rm-shortcode-id="a1e6445c7168d293a6da3f9600f534a2"><iframe type="lazy-iframe" data-runner-src="https://www.youtube.com/embed/H2f0Wd3zNj0?rel=0" width="100%" height="auto" frameborder="0" scrolling="no" style="position:absolute;top:0;left:0;width:100%;height:100%;"></iframe></span>
Many of the most popular apps are about self-improvement.
Emotions are the newest hot commodity, and we can't get enough.