Category Archives: automation

[] Dr. Mica Endsley: Current Challenges and Future Opportunities In Human-Autonomy Research

We had a chance to interview Dr. Mica Endsley about her thoughts on autonomy.

The social science research that we cover in this blog is carried out by a multitude of talented scientists across the world; each studying a different facet of the problem. In our second post in a new series, we interview one the leaders in the study of the human factors of autonomy, Dr. Mica Endsley.

Down on the farm: Human factors psychologist Margaux Ascherl optimizes technology to make farming more efficient

Complimenting the previous post about applied psychology, this new article dives into how one human factors PhD, Margaux Ascherl, is working to make farming more efficient with technology (she also happens to be my former student!):

The world’s population of 7.3 billion is predicted to grow to 9.7 billion by 2050, according to the Global Harvest Initiative. To feed all those people, global agricultural productivity must increase by 1.75 percent annually.

One person working to drive this increase is Margaux Ascherl, PhD, user experience leader at John Deere Intelligent Solutions Group in Urbandale, Iowa. John Deere recruited Ascherl in late 2012 while she was finishing her PhD in human factors psychology at Clemson University. Five years later, she now leads a team responsible for the design and testing of precision agriculture technology used in John Deere equipment.

Ascherl spoke to the Monitor about what it’s like to apply psychology in an agricultural context and how her team is helping farmers embrace new technology to feed the world.

Human-Robot/AI Relationships: Interview with Dr. Julie Carpenter

Over at, we had a chance to interview Dr. Julie Carpenter about her research on human-robot/AI relationships.

As the first post in a series, we interview one the pioneers in the study of human-AI relationships, Dr. Julie Carpenter. She has over 15 years of experience in human-centered design and human-AI interaction research, teaching, and writing. Her principal research is about how culture influences human perception of AI and robotic systems and the associated human factors such as user trust and decision-making in human-robot cooperative interactions in natural use-case environments.

Throwback Thursday: A model for types and levels of automation []

This week’s Throwback Thursday post (next door, at covers another seminal paper in the study of autonomy:

This is our second post on our “throwback” series. In this paper, I will take you through an article written by the best in the human factors and ergonomics field, the late Raja Parasuraman, Tom Sheridan, and Chris Wickens. Though several authors have introduced the concept of automation being implemented at various levels, for me this article nailed it.

Throwback Thursday: The Ironies of Automation []

My third job (in addition to being a professor, and curating this blog) is working on another blog with Arathi Sethumadhavan focused on the social science of autonomy and automation.  You can find us over here.

Occasionally, I will cross-post items that might be of interest to both readerships.  Over there, we’re starting a new series of posts called Throwback Thursdays where we go back in time to review some seminal papers in the history of human-automation interaction (HAI), but for a lay audience.

The first post discusses Bainbridge’s 1983 paper discussing the “Ironies of Automation”:

Don’t worry, our Throwback Thursday doesn’t involve embarrassing pictures of me or Arathi from 5 years ago.  Instead, it is more cerebral.  The social science behind automation and autonomy is long and rich, and despite being one of the earliest topics of study in engineering psychology, it has even more relevance today.

In this aptly titled paper, Bainbridge discusses, back in 1983(!), the ironic things that can happen when humans interact with automation.  The words of this paper ring especially true today when the design strategy of some companies is to consider the human as an error term to be eliminated


Did a User Interface Kill 10 Navy Sailors?

I chose a provocative title for this post after reading the report on what caused the wreck of the USS John McCain in August of 2017. A summary of the accident is that the USS John McCain was in high-traffic waters when they believed they lost control of steering the ship. Despite attempts to slow or maneuver, it was hit by another large vessel. The bodies of 10 sailors were eventually recovered and five others suffered injury.

Today marks the final report on the accident released by the Navy. After reading it, it seems to me the report blames the crew. Here are some quotes from the offical Naval report:

  • Loss of situational awareness in response to mistakes in the operation of the JOHN S MCCAIN’s steering and propulsion system, while in the presence of a high density of maritime traffic
  • Failure to follow the International Nautical Rules of the Road, a system of rules to govern the maneuvering of vessels when risk of collision is present
  • Watchstanders operating the JOHN S MCCAIN’s steering and propulsion systems had insufficient proficiency and knowledge of the systems

And a rather devestating:

In the Navy, the responsibility of the Commanding Officer for his or her ship is absolute. Many of the decisions made that led to this incident were the result of poor judgment and decision making of the Commanding Officer. That said, no single person bears full responsibility for this incident. The crew was unprepared for the situation in which they found themselves through a lack of preparation, ineffective command and control and deficiencies in training and preparations for navigation.


Ars Technica called my attention to an important but not specifically called out reason for the accident: the poor feedback design of the control system. I think it is a problem that the report focused on “failures” of the people involved, not the design of the machines and systems they used. After my reading, I would summarize the reason for the accident as “The ship could be controlled from many locations. This control was transferred using a computer interface. That interface did not give sufficient information about its current state or feedback about what station controlled what functions of the ship. This made the crew think they had lost steering control when actually that control had just been moved to another location.” I based this on information from the report, including:

Steering was never physically lost. Rather, it had been shifted to a different control station and watchstanders failed to recognize this configuration. Complicating this, the steering control transfer to the Lee Helm caused the rudder to go amidships (centerline). Since the Helmsman had been steering 1-4 degrees of right rudder to maintain course before the transfer, the amidships rudder deviated the ship’s course to the left.

Even this section calls out the “failure to recognize this configuration.” If the system is designed well, one shouldn’t have to expend any cognitive or physical resources to know from where the ship is being controlled.

Overall I was surprised at the tone of this report regarding crew performance. Perhaps some is deserved, but without a hard look at the systems the crew use, I don’t have much faith we can avoid future accidents. Fitts and Jones were the start of the human factors field in 1947, when they insisted that the design of the cockpit created accident-prone situations. This went against the beliefs of the times, which was that “pilot error” was the main factor. This ushered in a new era, one where we try to improve the systems people must use as well as their training and decision making. The picture below is of the interface of the USS John S McCain, commissioned in 1994. I would be very interested to see how it appears in action.

US Navy (USN) Boatswain’s Mate Seaman (BMSN) Charles Holmes mans the helm aboard the USN Arleigh Burke Class Guided Missile Destroyer USS JOHN S. MCCAIN (DDG 56) as the ship gets underway for a Friends and Family Day cruise. The MCCAIN is getting underway for a Friends and Family Day cruise from its homeport at Commander Fleet Activities (CFA) Yokosuka Naval Base (NB), Japan (JPN). Source: Wikimedia Commons

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.


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.

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

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.