Category Archives: automation

Designing the technology of ‘Blade Runner 2049’

The original Bladerunner is my favorite movie and can be credited as sparking my interest in human-technology/human-autonomy interactions.  The sequel is fantastic if you have not seen it (I’ve seen it twice already and soon a third).

If you’ve seen the original or sequel, the representations of incidental technologies may have seemed unusual.  For example, the technologies feel like a strange hybrid of digital/analog systems, they are mostly voice controlled, and the hardware and software has a well-worn look.  Machines also make satisfying noises as they are working (also present in the sequel).  This is a refreshing contrast to the super clean, touch-based, transparent augmented reality displays shown in other movies.

This really great post/article from Engadget [WARNING CONTAINS SPOILERS] profiles the company that designed the technology shown in the movie Bladerunner 2049.  I’ve always been fascinated by futuristic UI concepts shown in movies.  What is the interaction like?  Information density? Multi-modal?  Why does it work like that and does it fit in-world?

The article suggests that the team really thought deeply about how to portray technology and UI by thinking about the fundamentals (I would love to have this job):

Blade Runner 2049 was challenging because it required Territory to think about complete systems. They were envisioning not only screens, but the machines and parts that would made them work.

With this in mind, the team considered a range of alternate display technologies. They included e-ink screens, which use tiny microcapsules filled with positive and negatively charged particles, and microfiche sheets, an old analog format used by libraries and other archival institutions to preserve old paper documents.

 

Tesla counterpoint: “40% reduction in crashes” with introduction of Autosteer

I posted yesterday about the challenges of fully autonomous cars and cars that approach autonomy. Today I bring you a story about the successes of semi-automatic features in automobiles.

Tesla has a feature called Autopilot that assists the driver without being completely autonomous. Autopilot includes car-controlled actions such as collision warnings, automatic emergency braking, and automatic lane keeping. Tesla classifies the Autopilot features as Level 2 automation. (Level 5 is considered fully autonomous). Rich has already given our thoughts about calling this Autopilot in a previous post. One particular feature is called AutoSteer, described in the NHTSA report as:

The Tesla Autosteer system uses information from the forward-looking camera, the radar sensor, and the ultrasonic sensors, to detect lane markings and the presence of vehicles and objects to provide automated lane-centering steering control based on the lane markings and the vehicle directly in front of the Tesla, if present. The Tesla owner’s manual contains the following warnings: 1) “Autosteer is intended for use only on highways and limited-access roads with a fully attentive driver. When using Autosteer, hold the steering wheel and be mindful of road conditions and surrounding traffic. Do not use Autosteer on city streets, in construction zones, or in areas where bicyclists or pedestrians may be present. Never depend on Autosteer to determine an appropriate driving path. Always be prepared to take immediate action. Failure to follow these instructions could cause serious property damage, injury or death;” and 2) “Many unforeseen circumstances can impair the operation of Autosteer. Always keep this in mind and remember that as a result, Autosteer may not steer Model S appropriately. Always drive attentively and be prepared to take immediate action.” The system does not prevent operation on any road types.

An NHTSA report looking into a fatal Tesla crash also noted that the introduction of Autosteer corresponded to a 40% reduction in automobile crashes. That’s a lot considering Dr. Gill Pratt from Toyota said he might be happy with a 1% change.

Autopilot was enabled in October, 2015, so there has been a good period of time for post-autopilot crash data to be generated.

Toyota Gets It: Self-driving cars depend more on people than on engineering

I recommend reading this interview with Toyota’s Dr. Gill Pratt in its entirety. He discusses pont-by-point the challenges of a self-driving car that we consider in human factors, but don’t hear much about in the media. For example:

  • Definitions of autonomy vary. True autonomy is far away. He gives the example of a car performing well on an interstate or in light traffic compared to driving through the center of Rome during rush hour.
  • Automation will fail. And the less it fails, the less prepared the driver is to assume control.
  • Emotionally we cannot accept autonomous cars that kill people, even if it reduces overall crash rates and saves lives in the long run.
  • It is difficult to run simulations with the autonomous cars that capture the extreme variability of the human drivers in other cars.

I’ll leave you with the last paragraph in the interview as a summary:

So to sum this thing up, I think there’s a general desire from the technical people in this field to have both the press and particularly the public better educated about what’s really going on. It’s very easy to get misunderstandings based on words like or phrases like “full autonomy.” What does full actually mean? This actually matters a lot: The idea that only the chauffeur mode of autonomy, where the car drives for you, that that’s the only way to make the car safer and to save lives, that’s just false. And it’s important to not say, “We want to save lives therefore we have to have driverless cars.” In particular, there are tremendous numbers of ways to support a human driver and to give them a kind of blunder prevention device which sits there, inactive most of the time, and every once in a while, will first warn and then, if necessary, intervene and take control. The system doesn’t need to be competent at everything all of the time. It needs to only handle the worst cases.

Tesla is wrong to use “autopilot” term

Self driving cars are a hot topic!   See this Wikipedia page on Autonomous cars for a short primer.  This post is mainly a bit of exploration of how the technology is presented to the user.

Tesla markets their self driving technology using the term “Autopilot”.  The German government is apparently unhappy with the use of that term because it could be misleading (LA Times):

Germany’s transport minister told Tesla to cease using the Autopilot name to market its cars in that country, under the theory that the name suggests the cars can drive themselves without driver attention, the news agency Reuters reported Sunday.

Tesla wants to be perceived as first to market with a fully autonomous car (using the term Autopilot) yet they stress that it is only a driver assistance system and that the driver is meant to stay vigilant.  But I do not think term Autopilot is perceived that way by most lay people.  It encourages an unrealistic expectation and may lead to uncritical usage and acceptance of the technology, or complacency.

Complacency can be described and manifested as:

  • too much trust in the automation (more than warranted)
  • allocation of attention to other things and not monitoring the proper functioning of automation
  • over-reliance on the automation (letting it carry out too much of the task)
  • reduced awareness of one’s surroundings (situation awareness)

Complacency is especially dangerous when unexpected situations occur and the driver must resume manual control.  The non-profit Consumer Reports says:

“By marketing their feature as ‘Autopilot,’ Tesla gives consumers a false sense of security,” says Laura MacCleery, vice president of consumer policy and mobilization for Consumer Reports. “In the long run, advanced active safety technologies in vehicles could make our roads safer. But today, we’re deeply concerned that consumers are being sold a pile of promises about unproven technology. ‘Autopilot’ can’t actually drive the car, yet it allows consumers to have their hands off the steering wheel for minutes at a time. Tesla should disable automatic steering in its cars until it updates the program to verify that the driver’s hands are on the wheel.”

Companies must commit immediately to name automated features with descriptive—not exaggerated—titles, MacCleery adds, noting that automakers should roll out new features only when they’re certain they are safe.

Tesla responded that:

“We have great faith in our German customers and are not aware of any who have misunderstood the meaning, but would be happy to conduct a survey to assess this.”

But Tesla is doing a disservice by marketing their system using the term AutoPilot and by selectively releasing video of the system performing flawlessly:

Using terms such as Autopilot, or releasing videos of near perfect instances of the technology will only hasten the likelihood of driver complacency.

But no matter how they are marketed, these systems are just machines that rely on high quality sensor input (radar, cameras, etc).  Sensors can fail, GPS data can be old, or situations can change quickly and dramatically (particularly on the road).  The system WILL make a mistake–and on the road, the cost of that single mistake can be deadly.

Parasuraman and colleagues have heavily researched how humans behave when exposed to highly reliable automation in the context of flight automation/autopilot systems.  In a classic study, they first induced a sense of complacency by exposing participants to highly reliable automation.  Later,  when the automation failed, the more complacent participants were much worse at detecting the failure (Parasuraman, Molloy, & Singh, 1993).

Interestingly, when researchers examined very autonomous autopilot systems in aircraft, they found that pilots were often confused or distrustful of the automation’s decisions (e.g., initiating course corrections without any pilot input) suggesting LOW complacency.  But it is important to note that pilots are highly trained, and have probably not been subjected to the same degree of effusively positive marketing that the public is being subjected regarding the benefits of self-driving technology.  Tesla, in essence, tells drivers to “trust us“, further increasing the likelihood of driver complacency:

We are excited to announce that, as of today, all Tesla vehicles produced in our factory – including Model 3 – will have the hardware needed for full self-driving capability at a safety level substantially greater than that of a human driver. Eight surround cameras provide 360 degree visibility around the car at up to 250 meters of range. Twelve updated ultrasonic sensors complement this vision, allowing for detection of both hard and soft objects at nearly twice the distance of the prior system. A forward-facing radar with enhanced processing provides additional data about the world on a redundant wavelength, capable of seeing through heavy rain, fog, dust and even the car ahead.

To make sense of all of this data, a new onboard computer with more than 40 times the computing power of the previous generation runs the new Tesla-developed neural net for vision, sonar and radar processing software. Together, this system provides a view of the world that a driver alone cannot access, seeing in every direction simultaneously and on wavelengths that go far beyond the human senses.

References

Parasuraman, R., & Molloy, R. (1993). Performance consequences of automation-induced“complacency.” International Journal of Aviation Psychology, 3(1), 1-23.

Some other key readings on complacency:

Parasuraman, R. (2000). Designing automation for human use: empirical studies and quantitative models. Ergonomics, 43(7), 931–951. http://doi.org/10.1080/001401300409125

Parasuraman, R., & Wickens, C. D. (2008). Humans: Still vital after all these years of automation. Human Factors, 50(3), 511–520. http://doi.org/10.1518/001872008X312198

Parasuraman, R., Manzey, D. H., & Manzey, D. H. (2010). Complacency and Bias in Human Use of Automation: An Attentional Integration. Human Factors, 52(3), 381–410. http://doi.org/10.1177/0018720810376055

 

Human Factors Potpourri

Some recent items in the news with a human factors angle:

  • What happened to Google Maps?  Interesting comparison of Google Maps from 2010/2016 by designer/cartographer Justin O’Beirne.
  • India will use 3D paintings to slow down drivers.  Excellent use of optical illusions for road safety.
  • Death by GPS.  GPS mis-routing is the easiest and most relatable example of human-automaiton interaction.  Unfortunately, to its detriment, this article does not discuss the automation literature, instead focusing on more basic processes that, I think, are less relevant.

Wiener’s Laws

The article “The Human Factor” in Vanity Fair is two years old, but since I can’t believe I missed posting it — here it is! It’s a riveting read with details of the Air France Flight 447 accident and intelligent discussion of the impact automation has on human performance. Dr. Nadine Sarter is interviewed and I learned of a list of flight-specific “laws” developed by Dr. Earl Wiener, a past-president of HFES.

“Wiener’s Laws,” from the article and from Aviation Week:

  • Every device creates its own opportunity for human error.
  • Exotic devices create exotic problems.
  • Digital devices tune out small errors while creating opportunities for large errors.
  • Invention is the mother of necessity.
  • Some problems have no solution.
  • It takes an airplane to bring out the worst in a pilot.
  • Whenever you solve a problem, you usually create one. You can only hope that the one you created is less critical than the one you eliminated.
  • You can never be too rich or too thin (Duchess of Windsor) or too careful about what you put into a digital flight-guidance system (Wiener).
  • Complacency? Don’t worry about it.
  • In aviation, there is no problem so great or so complex that it cannot be blamed on the pilot.
  • There is no simple solution out there waiting to be discovered, so don’t waste your time searching for it.
  • If at first you don’t succeed… try a new system or a different approach.
  • In God we trust. Everything else must be brought into your scan.
  • It takes an airplane to bring out the worst in a pilot.
  • Any pilot who can be replaced by a computer should be.
  • Today’s nifty, voluntary system is tomorrow’s F.A.R.

Kudos to the author, William Langewiesche, for a well researched and well written piece.

Discussion of Human Factors on “Big Picture Science” podcast

You all know I love podcasts. One of my favorites, Big Picture Science, held an interview with Nicholas Carr (a journalist) on over-reliance in automation. The entire podcast, What the Hack, also covers computer security. To skip to the HF portion, click here.

  • +points for mentioning human factors by name
  • +points for clearly having read much of the trust in automation literature
  • -points for falling back on the “we automate because we’re lazy” claim, rather than acknowledging that the complexity of many modern systems requires automation for a human to be able to succeed. Do you want to have that flight to NY on the day you want it? Then we have to have automation to help that happen – the task has moved beyond human ability to accomplish it alone.
  • -points for the tired argument that things are different now. Google is making us dumber. Essentially the same argument that happens with every introduction of technology, including the printing press. We aren’t any different than the humans that painted caves 17,300 years ago.

For more podcasts on humans and automation, check out this recent Planet Money: The Big Red Button. You’ll never look at an elevator the same way.

*While looking up support for the claim that people have always thought their era was worse than the previous, I found this blog post. Looks like I’m not the first to have this exact thought.

Prominent figures warn of dangerous Artificial Intelligence (it’s probably a bad HF idea too)

Recently, some very prominent scientists and other figures have warned of the consequences of autonomous weapons, or more generally artificial intelligence run amok.

The field of artificial intelligence is obviously a computational and engineering problem: designing a machine (i.e., robot) or software that can emulate thinking to a high degree.   But eventually, any AI must interact with a human either by taking control of a situation from a human (e.g., flying a plane) or suggesting courses of action to a human.

I thought this recent news item about potentially dangerous AI might be a great segue to another discussion of human-automation interaction.  Specifically, to a detail that does not frequently get discussed in splashy news articles or by non-human-factors people:  degree of automation. This blog post is heavily informed by a proceedings paper by Wickens, Li, Santamaria, Sebok, and Sarter (2010).

First, to HF researchers, automation is a generic term that encompasses anything that carries out a task that was once done by a human.  Such as robotic assembly, medical diagnostic aids, digital camera scene modes, and even hypothetical autonomous weapons with AI.  These disparate examples simply differ in degree of automation.

Let’s back up for a bit: Automation can be characterized by two independent dimensions:

  • STAGE or TYPE:  What is it doing and how is it doing it?
  • LEVEL: How much it is doing?

Stage/Type of automation describes the WHAT tasks are being automated and sometimes how.  Is the task perceptual, like enhancing vision at night or amplifying certain sounds?  Or is the automation carrying out a task that is more cognitive, like generating the three best ways to get to your destination in the least amount of time?

The second dimension, Level, refers to the balance of tasks shared between the automation and the human; is the automation doing a tiny bit of the task and then leaving the rest to the user?  Or is the automation acting completely on its own with no input from the operator (or ability to override)?

If you imagine STAGE/TYPE (BLUE/GREEN) and LEVEL (RED) as the X and Y of a chart (below), it becomes clearer how various everyday examples of automation fit into the scheme.  As LEVEL and/or TYPE increase, we get a higher degree of automation (dotted line).

Degrees of automation (Adapted from Wickens et al., 2010)
Degrees of automation represented as the dotted line (Adapted from Wickens et al., 2010)

Mainstream discussions of AI and its potential dangers seem to be focusing on a hypothetical ultra-high degree of automation.  A hypothetical weapon that will, on its own, determine threats and act.  There are actually very few examples of such a high level of automation in everyday life because cutting the human completely “out of the loop” can have severely negative human performance consequences.

The figure below shows some examples of automation and where they fit into the scheme:

Approximate degrees of automation of everyday examples of automation
Approximate degrees of automation of everyday examples of automation

Wickens et al., (2010) use the phrase, “the higher they are, the farther they fall.”   This means that when humans interact with greater degrees of automation, they do fine if it works correctly, but will encounter catastrophic consequences when automation fails (and it always will at some point).  Why?  Users get complacent with high DOA automation, they forget how to do the task themselves, or they loose track of what was going on before the automation failed and thus cannot recover from the failure so easily.

You may have experienced a mild form of this if your car has a rear-backup camera.  Have you ever rented a car without one?  How do you feel? That feelings tends to get magnified with higher degrees of automation.

So, highly autonomous weapons (or any high degree of automation) is not only a philosophically bad/evil idea, it is bad for human performance!

 

Haikuman Factors

Sometimes it’s good to take a step back from the seriousness of our work and find new focus. H(aiku)man factors is the brainchild of my colleague Douglas Gillan. Each summarizes a concept in the field while following the haiku form of 5-7-5 and an emphasis on juxtoposition and inclusion of nature. Enjoy and contribute your own in the comments!

H(aik)uman Factors3

H(aik)uman Factors2

H(aik)uman Factors

H(aik)uman Factors6

H(aik)uman Factors5

H(aik)uman Factors4

All of the above are by Doug Gillan.

Other contributions:

Inattentional blindness by Allaire Welk
Unicycling clown
Challenging primary task
Did you notice it?

Affordances by Lawton Pybus
round, smooth ball is thrown
rolls, stops at the flat, wing-back
chair on which I sit

Escalation by Olga Zielinska
headache, blurred vision
do not explore Web MD
it’s not a tumor

Automatic Processing by Anne McLaughlin
end of the workday
finally get to go home
arugh, forgot groceries

Automation by Richard Pak
Siri, directions!
No wait, I’ll get it myself
Drat, I forgot how

Prospective Memory by Natalee Baldwin
I forgot the milk!
Prospective memory failed
Use a reminder

Working Memory by Will Leidheiser
copious knowledge.
how much can I remember?
many things at once.

Radio interview with Rich

Our own Rich Pak was interviewed by the Clemson radio show “Your Day.”

Audio clip: Adobe Flash Player (version 9 or above) is required to play this audio clip. Download the latest version here. You also need to have JavaScript enabled in your browser.

They cover everything from the birth of human factors psychology to the design of prospective memory aids for older adults. Enjoy!