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Category: Automotive

“Potemkin Maps”: Impress foreign dignitaries and out-of-town investors by following a GPS map route through a misleadingly-nice part of your city!

Background:

Phone map apps often have a few optional settings for a route, such as:

  • Avoid highways (for driving)
  • Fewer bus transfers (for public transit)
  • Avoid hills (for walking)

The issue:

Sometimes, you want drive on the most scenic route from point A to point B, without too much concern about efficiency.

For example, you might want to impress an out-of-town guest, or hide the seedier parts of a city from a visiting foreign dignitary or investor.

Proposal:

The “scenic route” to a destination attempts to route you through the highest-economic-value areas that it can find.

This method, called the “Potemkin Route” after the 1787 idea of the same name, uses the following data:

  • Tax records (to find the highest property values)
  • The police blotter (to avoid areas of high crime)
  • Elevation maps (to look for scenic views)

Then, it routes you to the optimum area to show off the most appealing areas of the region near your route (user interface mockup in Figure 1).

 

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Fig. 1: If you select both [AVOID HIGHWAYS] and [AVOID LOW PROPERTY VALUES], as the user has in this example, your route might be substantially longer.

Conclusion:

You could use this route yourself, even if you aren’t trying to impress a foreign dignitary.

PROS: Allows you to ignore the problems of your city.

CONS: Allows you to ignore the problems of your city.

If you obey the demands of this phone app, you’ll never have to wait at a stoplight again! If you are a pedestrian, anyway. Might also work for bicyclists and drivers!

Background:

In most American cities, four-way intersections with stoplights are the most common form of traffic control.

The issue:

As a pedestrian, these intersections are frustrating: if the stoplights are not synchronized, you’ll randomly encounter red lights while walking from block to block. But even when lights are synchronized, they are synchronized for car driving speeds. Thus, at normal walking speed, a pedestrian will inevitably spend a large fraction of travel time waiting at crosswalks for the light to turn green.

Although a pedestrian can increase or decrease their walking speed, it is difficult to select an optimal speed without knowing exactly when the light will change.

Proposal:

Fortunately, a phone app can easily measure walking speed and distance to the next traffic light, and then display a recommended walking speed that will get a pedestrian to the light when it is green (Figure 1).

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Fig. 1: Since this phone knows how far the next light is and exactly when the light will change, it can recommend a walking pace that will get its owner to the light while the light is green. The green / gray arrow in the middle of the screen is a “progress bar,” showing the pedestrian’s current position relative to the previous intersection (base of arrow) and the next light (tip of arrow).

 

Using this app, a person can enjoy both a more leisurely pace at lights they’d miss anyway, and can walk ever-so-slightly faster (Figure 2) in order to make it through intersections just before the light turns red.

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Fig. 2: In the top example (A), a pedestrian walks at a uniform pace that causes them to have to wait at two of the three lights. In the bottom example (B), the pedestrian is using our new app, and adjusts their walking speed to hit all the lights while they are green. Recommended walking speed is shown by the orange bar at the very bottom.

Conclusion:

This type of app would probably work for drivers and bicyclists as well (ideally through audio instructions).

PROS: Encourages walking in cities, thus improving national cardiovascular fitness.

CONS: Users of this app might wait at fewer lights, but would be at higher risk of being run over by a car / bicyclist / steamroller while distracted by the app’s various recommendations and statistics.

Stop getting hit by self-driving cars with this one fashion trick that involves putting weird labels on all your clothing! Don’t be the last one to catch on to this new fashion trend.

Background:

In a hypothetical future where self-driving cars are increasingly common, they’ll have to do a really good job of automatically distinguishing between things that require sudden braking (e.g. a person in the roadway) and things that are OK to hit (e.g. a tumbling empty cardboard box).

The issue:

This is a hard problem. When a car gets data from its various cameras (and other sensors), it needs to figure out what exactly it is that it is seeing (Figure 1).

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Fig. 1: This is probably a pedestrian in the roadway, but could it also be a billboard advertisement hundreds of feet away?

Although the specific “distant-billboard-or-close-pedestrian” question in Figure 1 can be answered just by using two cameras to estimate distance, there are situations where the problem must be resolved in a more complex fashion (Figure 2).

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Fig. 2: Top: the image is interpreted correctly, and the car does NOT hit the pedestrian. Bottom: the car incorrectly believes that it sees a sunflower, and collides with it at full speed. Lest you think this is totally implausible, check out some specially-crafted adversarial examples (that can turn a panda into a banana) and a method of tricking lane-following algorithms into swerving the car into oncoming traffic.

Proposal:

We propose to place special “this is a human” symbols on articles of clothing that a human might wear (Figure 3).

When a car sees one of these unusual QR-code-like symbols, it will instantly say “ah, sunflowers do not wear specially-marked shoes, time to hit the brakes!”

To avoid this becoming a fashion disaster, these markings would not be apparently at normal human-visible wavelengths of light, but would only be detectable by special camera equipment.

Perhaps the markings could have fluorescent ink in them, and all cars could drive around with UV lights in the front.

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Fig. 3: Left: this is what the shoe looks like to a human—the markings are invisible to the naked eye. Middle: the camera can see wavelengths of light beyond human ability, and can detect these special markings (shown here as yellow checkerboards). Right: the camera sees the checkerboard, and the object-classification algorithm realizes that this shoe is likely to be attached to a human.

One common objection to many self-driving-car-related issues is “couldn’t some criminal put these markers all over the city, to trick self-driving cars?”

The answer is yes, but it would be as equally illegal as it currently is to put mannequins on a winding road (which would also confuse human drivers).

Conclusion:

This might be redundant with an infrared camera—in most locations, a human already is obviously distinguished from the background environment just by their warm-blooded glow in the infrared spectrum.

PROS: This will definitely make me a ton of money when it is licensed by major car manufacturers. Also, would someone please apply for and pay for a patent on my behalf? Thanks!

CONS: If one of these specially-marked shoes falls onto the roadway (perhaps by falling out of someone’s messenger bag while they’re biking), do we really want every car to come to a screeching halt at the sight of a single unattached shoe?

 

 

Make your carpool / ride-sharing commute even safer with this amazing plan to add strobe lights to your car—legally! Bicyclists love this one weird tip!

The issue:

One ever-present hazard for bicyclists is the possibility of being “doored”—hit by a suddenly-opened driver’s side door of a parked car.

A similar issue confounds carpool passengers: when exiting a full vehicle, the driver’s-side passenger must open the door directly into traffic (since they cannot exit on the curb side). This presents the obvious risk of being hit by a car that is swerving around the temporarily-parked carpool vehicle, as shown in Figure 1.

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Fig. 1: A) The ride-sharing vehicle (blue) is stopped in the farthest-curbside lane, and a passenger is about to exit. A fast approaching-car (red) in the same lane is about to swerve around the parked car. B) The passenger opens the door (purple) and will step out into traffic. C) The red car collides with the open door.

There may be a lot of blame to assign in the scenario in Figure 1 (“the passenger should have waited longer before opening the door” or “the red car shouldn’t have gone around the stopped car”), but it’s easy to see how it would occur without any egregious negligence.

Proposal:

In order to make it obvious that a car door may be opening soon (i.e., that there is an occupant associated with a door of a stopped or nearly-stopped car), the following is proposed:

  • A row of lights are placed on the edges of the car, near the doors. These lights must be easily visible from behind the vehicle.
  • When the door handle is operated, these edge lights flash (see Figure 2). This would provide ~1–2 additional seconds for a driver or bicyclist to react before hitting the door.
  • Optionally, weight sensors in the car seats could detect whether or not someone is likely to exit via a specific door (if there are no passengers in the car, there is no reason for any of the lights to flash except for the ones on the driver’s door). Weight sensors are already used to decide whether or not to deploy passenger air bags, so this wouldn’t be a huge engineering challenge.
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Fig. 2: Flashing lights on the edge of the car can notify other drivers and bicyclists that a door might be opening soon (or is actively being opened).

Conclusion:

If you own an LED manufacturing plant, you should lobby your local government to make this feature mandatory, and try to avoid letting anyone do any scientific research to determine whether or not it’s actually effective.

PROS: Creates a new source of revenue for the LED light industry.

CONS: It is likely that there would be so many false positives—flashing lights for stopped cars at nearly every intersection, for example—that everyone would tune out these ubiquitous and uninformative warnings.

Improve the odds of finding a lost pet with this over-engineered license-plate-based system! The ultimate computer vision project for a machine learning startup.

Background:

“Lost cat” and “lost dog” signs are often placed up on telephone poles (Fig. 1), but it’s unlikely that a specific person who sees a lost pet will also have seen the sign (or even know that the pet is actually lost in the first place).

 

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Fig. 1: A person who sees this sign will know to be on the lookout for a lost snake, but the chances of seeing both the snake AND the poster are quite low.

Proposal:

In order to add more people to the lost-pet-searching process, the proposed system is as follows:

On the searchers side:

  • Car owners can add a camera to their car (see license plate example in Figure 2) that constantly scans for unidentified animals. This requires no effort on the part of the driver.
  • The camera saves snapshots and GPS coordinates for every animal it sees, and uploads these to a “Find a Lost Pet” web site. Many of these animals are probably not lost, or even pets!

On the pet-recoverers side:

  • Anyone with a lost pet can post the details of their lost animal and a reward to the “Find a Lost Pet” site. Ideal information would include a photo, approximate location, and the owner’s contact information.

Once the “Find a Lost Pet” image analysis system detects a match between an uploaded image and a lost pet, a “bounty” is issued for the recovery of that pet, and nearby drivers are notified.

Finally, assuming the animal is safely returned in the same number of pieces that it was expected to be in (generally this number is “one”), the bounty is split three ways: the web site, camera owner, and animal-recoverer all get a fraction of the total reward. This aligns everyone’s incentives and encourages people to install pet-scanning cameras in the hope of a payout.

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Fig. 2: This license plate camera is a “dog-scanner” camera that is constantly on the lookout for unidentified potentially-lost animals. Backup cameras like this already exist, so producing the hardware for this system would be relatively straightforward.

PROS: This system will help find lost pets, and definitely won’t be repurposed to create a totalitarian police state.

CONS: Not especially useful in finding burrowed or aquatic animals, so try not to lose one of those.

For your next job application / rental apartment selection / house purchase: you would be able to make an INFORMED decision about your commute thanks to this incredible piece of software!

Background:

In the United States, an employed person has two conflicting goals:

  1. To commute to their job as fast as possible (ideally by “hyperloop” or helicopter),
  2. …and to live as far away from their workplace as possible.

To these ends, thousands of man-hours have gone into new legislation preventing residences near places of employment (zoning laws which help with goal #2, above) and to developing new and complex commute-easing technologies such as self-driving cars or trains that travel at a thousand miles per hour (addressing goal #1).

The issue:

When accepting a new job, it’s hard to know how long or unpleasant your commute might be.

Although a person can get an idea of the total amount of time a commute is expected to take by checking an online map service, it’s a different matter to actually experience the commute.

Proposal:

In order to figure out if a commute is tolerable, a “Commute Test Drive” is proposed: this is just a piece of software that generates a realistically-long commute on the route that you specify (example in Figure 3, perhaps using data from OpenStreetMap) and then requires that you drive it in real-time.

 

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Fig. 1: This “Commute Test Drive” commute simulator would be similar to the delivery truck game “Euro Truck Simulator,” but with realistically-excruciatingly-large maps.

If a person wants to use public transit instead of driving, then a more sophisticated version of this software might allow the player to simulate the process of walking to a bus stop, waiting for a bus, and sitting on the bus for the correct amount of time.

By enduring the commute in the comfort of their own home (Fig. 2), a person can make a better-educated decision about accepting a job (or buying / renting a house) in a given area.

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Fig. 2: Although it would be possible to play this simulator with a gamepad or a mouse and keyboard, the steering wheel adds realism.

 

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Fig. 3: The route would be simulated with traffic and any other elements of a commute that might cause a delay (like railroad crossings, police checkpoints, and drawbridges).

Conclusion:

PROS: Inexpensively allows a person to make informed decisions about where to live and work.

CONS: This software probably already exists in some form as a fan-made Euro Truck Simulator mod.

Stop getting run over by those passenger-transport golf carts in airport concourses with this one incredible tip, brought to you by the Big Laser Pointer industry.

Background:

Airport terminals often have small golf-cart-like trams that can be driven around in the passenger concourses. These are often used to help people move around the concourses (for example, one might be used to help a passenger with a leg in a cast who is trying to make it to a connecting flight).

The issue:

These passenger carts can move quickly, and may run over pedestrians in the terminal. To help prevent this, the carts usually emit an incredibly loud and annoying beep (like a truck backing up).

However, it is usually not very obvious where a cart is based only on the annoying beeping sound, especially in a crowded concourse (Figure 1).

 

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Fig. 1: The annoying beeping coming from this airport golf cart lets people know that a cart is nearby, but requires pedestrians to 1) find the cart and 2) figure out what path the cart is attempting to take through the airport crowds.

 

Proposal:

Instead of only beeping, the passenger cart could also have a special set of headlights that would project a “danger zone” image in front of them. This would make it extremely obvious as to where pedestrians should not walk.

 

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Fig. 2: This updated passenger cart has special headlights that project a “danger zone” region in the path of travel of the cart. These headlights could be repurposed laser pointers with a more spread out pattern (instead of a single dot).

Conclusion:

These new headlights could be an after-market attachment, since most airports will probably not want to replace their existing fleet of golf carts.

The light would only turn on when the shuttle is moving and would only consume as much energy as ~10 handheld laser pointers, so it shouldn’t substantially reduce cart battery life.

PROS: Would make it much easier to avoid being run over by an airport carts.

CONS: Probably… none? Is this a legitimately good idea?

 

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Fig. 3 (bonus): Illustration for a hypothetical patent application.