‡ These authors contributed equally to the research conceptualization and investigation and to editing the manuscript.
By adopting and extending lessons from the air traffic control system, we argue that a nationwide remote monitoring system for driverless vehicles could increase safety dramatically, speed these vehicles’ deployment, and provide employment. It is becoming clear that fully driverless vehicles will not be able to handle “edge” cases in the near future, suggesting that new methods are needed to monitor remotely driverless vehicles’ safe deployment. While the remote operations concept is not new, a super-human driver is needed to handle sudden, critical events. We envision that the remote operators do not directly drive the vehicles, but provide input on high level tasks such as path-planning, object detection and classification. This can be achieved via input from multiple individuals, coordinated around a task at a moment’s notice. Assuming a 10% penetration rate of driverless vehicles, we show that one remote driver can replace 14,840 human drivers. A comprehensive nationwide interoperability standard and procedure should be established for the remote monitoring and operation of driverless vehicles. The resulting system has potential to be an order of magnitude safer than today’s ground transportation system. We articulate a research and policy roadmap to launch this nationwide system. Additionally, this hybrid human–AI system introduces a new job category, likely a source of employment nationwide.
The paper uses publicly available US survey data:
By adopting and extending lessons from the air traffic control system, a nationwide remote monitoring system for driverless vehicles could dramatically increase safety, speed deployment of these vehicles, and provide a source of employment in this nascent industry. In 2018, California became the first state in America to permit driverless vehicle testing on public roads. Sensibly, state law [
“There is a communication link between the vehicle and the remote driver to provide information on the vehicle’s location and status and allow two-way communication between the remote driver and any passengers if the vehicle experiences any failures that would endanger the safety of the vehicle’s passengers or other road users, or otherwise prevent the vehicle from functioning as intended, while operating without a driver.”
Furthermore, the permit holder must provide to remote drivers:
“Instruction on the automated driving system technology being tested, including how to respond to emergency situations and hazardous driving scenarios that could be experienced by the vehicle or the vehicle’s occupants.”
Remote drivers play an important role as “traffic control” to monitor, plan, and possibly actively support the safety of driverless vehicle passengers and other road users. While we acknowledge possible security vulnerabilities of this approach, we call on the research and technical communities to develop secure means to enable remote monitoring and operations. Regrettably, recent legislation in Nebraska [
On Sunday, March 18, 2018, at 10pm a self-driving vehicle operated by Uber struck and killed 49-year-old Elaine Herzberg in Tempe, Arizona, as she walked across a lane of traffic. A safety driver in the car, not attending to the road, was alerted of the pending crash too late [
We argue that a teleoperations system for a fleet of driverless vehicles would efficiently use those 4.7 seconds to improve road safety and reduce crash severity. Recent insights into human-assisted artificial intelligence (AI) systems establish the feasibility of response times less than 0.3 seconds (roughly equivalent to the average human reaction time, but more stable and less subject to distraction) by combining reinforcement learning with crowd feedback [
Remote operator and monitoring systems are already in common use.
Automated vehicles promise a host of societal benefits, including dramatically improved safety, increased accessibility, greater productivity, and higher quality of life. In order to deliver on these promises, the vehicles must be able to operate and reason over a nearly infinite number of known and unknown potential conflict situations. By adopting lessons and experiences from the air traffic control system, we argue that using humans to supervise driverless vehicles is 1) technically feasible, 2) necessary to achieve safety goals, and 3) an available source of employment [
Fully autonomous (Level 5) vehicle control is a relatively distant goal, as it requires AI to both understand scenarios involving people and other objects in the environment and know how to respond. Current autonomous vehicles (AVs) can drive quite well in typical (frequently encountered) settings but fail in exceptional cases. Worse, these exceptional cases are often the most dangerous and may arise suddenly, leaving human drivers with only a couple of seconds, at most, to react—precisely a setting in which people can be expected to perform worst. Compared to precautionary takeovers, these sudden scenarios already comprise most of the disengagements that GM Cruise reported in 2018, accounting for 53.5% of autonomous driving interruptions [
Our team’s current work explores how AI-coordinated groups of remote drivers might best attain superhuman collective performance, overcoming previously insurmountable barriers of human and network latency. By leveraging AVs’ “understanding” of the world (e.g., state space representation, transition model), human effort/insight can be guided toward reachable future states of the world. This allows us to simulate potential situations mere seconds or even fractions of a second before they occur, and cache responses indicating to the AV how a human would respond locally in such a situation—the result being an ability to leverage human responses in milliseconds rather than seconds, opening a whole new frontier for possible applications to critical, life-saving scenarios. See [
A naive approach to staffing remote drivers would dedicate one to each vehicle in the fleet. Less extreme could be one agent per active vehicle. However, even this is quite extreme: that all vehicles would require simultaneous assistance is highly unlikely. Thus, we now demonstrate a staffing approach that is significantly more efficient without taking on significant risk. Using the 2017 National Household travel survey (NHTS) [
Rank | Metropolitan area | Total annual miles driven in area (Millions) | Number of remote drivers needed (Standard arrivals) | Number of remote drivers needed (Bursty arrivals) | Number of remote drivers needed (Highly bursty arrivals) |
---|---|---|---|---|---|
1 | New York, NY | 93,512 | 103 | 111 | 122 |
2 | Los Angeles, CA | 71,791 | 83 | 89 | 100 |
3 | Dallas, TX | 50,231 | 62 | 67 | 76 |
4 | Chicago, IL | 49,348 | 61 | 66 | 75 |
5 | Atlanta, GA | 42,547 | 54 | 59 | 67 |
6 | Houston, TX | 42,431 | 54 | 59 | 67 |
7 | Washington, DC | 41,199 | 53 | 58 | 66 |
8 | Minneapolis, MN | 34,540 | 46 | 51 | 58 |
9 | Philadelphia, PA | 32,781 | 44 | 49 | 56 |
10 | Phoenix, AZ | 31,408 | 43 | 47 | 54 |
Calculated by time of day in the Erlang-B model. All times normalized to Eastern Standard Time.
An industry of AV software and hardware makers exists, as well as several startups developing teleoperations systems for driverless vehicles. While remote operations itself is not a new concept, what is needed is a super-human driver for sudden, critical events. This can be achieved via input from multiple individuals, coordinated around a task at a moment’s notice. We now detail three key building blocks that are required to achieve this vision: human-assisted AI, the human element, and system-level organization.
With the rise of artificial intelligence as a service, human-backed algorithms at scale have become the norm rather than the exception for intelligent systems. Google, Facebook, Apple (Siri), Samsung, Bloomberg, and countless other organizations use large groups of human annotators and checkers to ensure their intelligent services’ quality and reliability. However, while reliability and accuracy are important in all these settings, none of the prior methods has leveraged low-latency, real-time systems to provide input faster than any one person alone could.
Here’s how it can work— Software in the autonomous vehicle would analyze real-time vehicle data and electronically estimate the likelihood of “disengagement”—due to a situation in which the car’s automated systems might need human help—10–30 seconds in the future. If the likelihood exceeds a pre-set threshold, the system contacts a remotely located control center, sending data from the car. One or more remote drivers are assigned to resolve the pending disengagement. The control center’s system analyzes the car’s data, generates several possible scenarios, and provides them to several human supervisors situated in driving simulators. The remote drivers respond to the simulations and their responses are sent back to the vehicle. The vehicle now has a library of human-generated responses that it can choose from instantaneously, based on information from on-board sensors.
Previous work establishes the feasibility of this approach in low-latency environments. Responses in < 0.3 s (roughly equivalent to the human reaction time, but more stable and less subject to distraction) by combining reinforcement learning with crowd feedback [
The proposed system asks groups of remote drivers to help concurrently with a given monitored or control task in as little as ∼350 ms of a need’s arising. With video-based remote control latencies as low as 100 ms, total latency for control can be under 0.5 s. Thus, in any environment where we can predict possible outcomes 0.5 s in the future, “instant” responses become possible. This scope of settings is far larger than those we can observe prior to deployment. Prior work learned how to effectively interleave and combine groups’ input over short time spans [
To improve response speed, methods are needed to directly leverage the AV’s ability to understand possibilities that may arise in real settings (even when the system does not know how to respond to a possible setting) to pre-fetch possible configurations of the world. Using these future states, remote drivers can (in parallel) provide feedback before a system needs to know what action to take. What makes this possible is the speed of existing real-time staffing approaches. While 0.5 s may be a relatively slow response time for an engaged driver to respond to an event (usually accomplished within 200-300 ms), an ability to respond this quickly means that we need only pre-fetch future states of the world.
Recent work has shown that just-in-time (JIT) training can result in an average response time below 3.5 ms, reducing latency by three orders of magnitude [
Air traffic controllers are well known to have one of the most stressful jobs in the world [
While much research has been done on air traffic controllers, more will be needed to understand how remote drivers perform on monitoring and operating tasks. Performance measures should include evaluating accuracy, service time, cognitive load, and fatigue resulting from processing service requests. Understanding interactions of the above factors within a single person or team is critical to the safety performance of the teleoperations system. The findings of the human factors research should inform a licensing standard for remote drivers. The state of California statute requires the testing permit holder to document and certify that the remote drivers have adequate training and education. Such an approach creates standards of safety and certification needed to create a professional job category for remote drivers. Further research is needed to estimate the number of available workers with requisite cognitive and emotional skills, and judgement to become remote drivers.
This teleoperations system might have several possible operating models. In one, private companies who own or operate driverless vehicles would also own and operate remote assistance centers. This model is similar to the current GM OnStar system, in which only GM-equipped vehicles can access OnStar. This approach would allow the industry to compete on safety. The teleoperations system would be a feature that users or fleet purchasers can choose much like adaptive cruise control (ACC). However, this approach would likely lead to balkanized teleoperations systems that do not talk to each other. Standards groups like the Society of Automotive Engineers (SAE) or the International Organization for Standardization (ISO) would need to set standards to promote interoperability and communication between teleoperations systems. Additionally, this approach would require employing more drivers, due to a scale smaller than a centralized system.
Another model resembles the air traffic control system operated by the Federal Aviation Administration (FAA). Vehicle support tasks are split between workers at various local, regional, and national centers. As a vehicle moves between various locations, oversight is handed off between centers. A private firm under contract with the federal government could operate this system. If an example is wanted, a private nonprofit runs the nationwide air traffic control system in Canada [
We base the staffing estimates on 2017 nationwide passenger vehicle driving statistics and the disengagements generated from daily passenger travel in the United States. The National Household Travel Survey (NHTS) provides the annual miles driven for each hour of the day. We aggregate all the demand by shifting times to Eastern Standard Time (EST), and focus this analysis on the 52 metropolitan statistical areas (MSA) with more than 1 million people. We develop two queueing models to estimate the remote driver staffing levels [
We model the number of remote drivers needed as an Erlang-loss queueing system. We assume, for the sake of simplicity, that requests for service to our dynamic queueing system is driven by a stationary Poisson process with rate λ. This allows us to perform staffing calculations for peak hour demand. In the subsequent subsection we discuss how to extend these computations to time-varying staffing settings, but for now let us consider the peak hour.
In this section, we will discuss the generalization to non-stationary arrival rates. In doing so, we derive closed form formulas for mean queue length of the
Recent work by [
In addition to the Erlang-B model, we can also use the Erlang-C model for situations in which vehicles wait for an agent to provide service. In this section, we assume the inter-arrival and service time distributions are exponential, with rates λ and
Core Based Statistical Area (CBSA) | Annual Miles Driven (mil.) | Percent National Miles Driven | Erlang B | Erlang C (delay) | Erlang C (mean wait) | Erlang C (excess wait) | Normal Approx. ( | Normal Approx. ( | Normal Approx. ( | Normal Approx. ( | Normal Approx. ( |
---|---|---|---|---|---|---|---|---|---|---|---|
Atlanta-Sandy Springs-Roswell | 42,547 | 2.0 | 54 | 56 | 66 | 66 | 45 | 49 | 53 | 59 | 67 |
Austin-Round Rock | 18,664 | 0.9 | 29 | 30 | 46 | 46 | 22 | 25 | 28 | 32 | 38 |
Baltimore-Columbia-Towson | 17,217 | 0.8 | 27 | 28 | 45 | 45 | 21 | 23 | 26 | 30 | 36 |
Birmingham-Hoover | 9,087 | 0.4 | 18 | 18 | 38 | 38 | 13 | 14 | 17 | 20 | 24 |
Boston-Cambridge-Newton | 25,440 | 1.2 | 36 | 38 | 52 | 52 | 29 | 32 | 35 | 40 | 47 |
Buffalo-Cheektowaga-Niagara Falls | 5,370 | 0.3 | 13 | 13 | 35 | 35 | 9 | 10 | 12 | 14 | 18 |
Charlotte-Concord-Gastonia | 17,968 | 0.9 | 28 | 29 | 45 | 45 | 22 | 24 | 27 | 31 | 37 |
Chicago-Naperville-Elgin | 49,348 | 2.3 | 61 | 63 | 71 | 71 | 51 | 55 | 60 | 66 | 75 |
Cincinnati | 17,080 | 0.8 | 27 | 28 | 45 | 45 | 21 | 23 | 26 | 30 | 36 |
Cleveland-Elyria | 14,238 | 0.7 | 24 | 25 | 42 | 42 | 18 | 20 | 23 | 27 | 32 |
Columbus | 15,759 | 0.7 | 26 | 27 | 44 | 44 | 20 | 22 | 25 | 29 | 34 |
Dallas-Fort Worth-Arlington | 50,231 | 2.4 | 62 | 64 | 72 | 72 | 52 | 56 | 61 | 67 | 76 |
Denver-Aurora-Lakewood | 18,972 | 0.9 | 29 | 30 | 46 | 46 | 23 | 25 | 28 | 33 | 38 |
Detroit-Warren-Dearborn | 26,001 | 1.2 | 37 | 38 | 52 | 52 | 30 | 32 | 36 | 41 | 48 |
Grand Rapids-Wyoming | 7,866 | 0.4 | 16 | 17 | 37 | 37 | 11 | 13 | 15 | 18 | 22 |
Hartford-West Hartford-East Hartford | 7,843 | 0.4 | 16 | 17 | 37 | 37 | 11 | 13 | 15 | 18 | 22 |
Houston-The Woodlands-Sugar Land | 42,431 | 2.0 | 54 | 56 | 66 | 66 | 45 | 48 | 53 | 59 | 67 |
Indianapolis-Carmel-Anderson | 10,398 | 0.5 | 19 | 20 | 39 | 39 | 14 | 16 | 18 | 22 | 26 |
Jacksonville | 8,134 | 0.4 | 17 | 17 | 37 | 37 | 12 | 13 | 15 | 19 | 23 |
Kansas City | 10,969 | 0.5 | 20 | 21 | 40 | 40 | 15 | 17 | 19 | 22 | 27 |
Las Vegas-Henderson-Paradise | 8,809 | 0.4 | 17 | 18 | 38 | 38 | 12 | 14 | 16 | 19 | 24 |
Los Angeles-Long Beach-Anaheim | 71,791 | 3.4 | 83 | 86 | 90 | 90 | 72 | 76 | 82 | 89 | 100 |
Louisville/Jefferson County | 10,274 | 0.5 | 19 | 20 | 39 | 39 | 14 | 16 | 18 | 22 | 26 |
Memphis | 6,386 | 0.3 | 14 | 15 | 36 | 36 | 10 | 11 | 13 | 16 | 20 |
Miami-Fort Lauderdale-West Palm Beach | 28,918 | 1.4 | 40 | 42 | 55 | 55 | 32 | 35 | 39 | 44 | 51 |
Milwaukee-Waukesha-West Allis | 9,509 | 0.5 | 18 | 19 | 38 | 38 | 13 | 15 | 17 | 20 | 25 |
Minneapolis-St. Paul-Bloomington | 34,540 | 1.6 | 46 | 48 | 59 | 59 | 38 | 41 | 45 | 51 | 58 |
Nashville-Davidson-Murfreesboro–Franklin | 12,120 | 0.6 | 21 | 22 | 41 | 41 | 16 | 18 | 20 | 24 | 29 |
New Orleans-Metairie | 5,528 | 0.3 | 13 | 14 | 35 | 35 | 9 | 10 | 12 | 15 | 18 |
New York-Newark-Jersey City | 93,512 | 4.4 | 103 | 107 | 108 | 108 | 91 | 96 | 102 | 111 | 122 |
Oklahoma City | 11,237 | 0.5 | 20 | 21 | 40 | 40 | 15 | 17 | 19 | 23 | 27 |
Orlando-Kissimmee-Sanford | 16,728 | 0.8 | 27 | 28 | 44 | 44 | 20 | 23 | 26 | 30 | 35 |
Philadelphia-Camden-Wilmington | 32,781 | 1.6 | 44 | 46 | 58 | 58 | 36 | 39 | 43 | 49 | 56 |
Phoenix-Mesa-Scottsdale | 31,408 | 1.5 | 43 | 44 | 57 | 57 | 35 | 38 | 42 | 47 | 54 |
Pittsburgh | 11,955 | 0.6 | 21 | 22 | 40 | 40 | 16 | 18 | 20 | 24 | 29 |
Portland-Vancouver-Hillsboro | 17,096 | 0.8 | 27 | 28 | 45 | 45 | 21 | 23 | 26 | 30 | 36 |
Providence-Warwick | 9,966 | 0.5 | 19 | 20 | 39 | 39 | 14 | 15 | 18 | 21 | 26 |
Raleigh | 12,675 | 0.6 | 22 | 23 | 41 | 41 | 16 | 18 | 21 | 25 | 30 |
Richmond | 10,501 | 0.5 | 19 | 20 | 39 | 39 | 14 | 16 | 18 | 22 | 26 |
Riverside-San Bernardino-Ontario | 25,856 | 1.2 | 37 | 38 | 52 | 52 | 29 | 32 | 36 | 41 | 47 |
Rochester | 6,792 | 0.3 | 15 | 15 | 36 | 36 | 10 | 12 | 14 | 17 | 20 |
Sacramento–Roseville–Arden-Arcade | 16,946 | 0.8 | 27 | 28 | 45 | 45 | 21 | 23 | 26 | 30 | 36 |
Salt Lake City | 6,616 | 0.3 | 15 | 15 | 36 | 36 | 10 | 11 | 14 | 16 | 20 |
San Antonio-New Braunfels | 16,679 | 0.8 | 27 | 28 | 44 | 44 | 20 | 23 | 26 | 30 | 35 |
San Diego-Carlsbad | 22,605 | 1.1 | 33 | 35 | 49 | 49 | 26 | 29 | 32 | 37 | 43 |
San Francisco-Oakland-Hayward | 28,735 | 1.4 | 40 | 41 | 54 | 54 | 32 | 35 | 39 | 44 | 51 |
San Jose-Sunnyvale-Santa Clara | 13,442 | 0.6 | 23 | 24 | 42 | 42 | 17 | 19 | 22 | 26 | 31 |
Seattle-Tacoma-Bellevue | 17,773 | 0.8 | 28 | 29 | 45 | 45 | 22 | 24 | 27 | 31 | 37 |
St. Louis | 19,770 | 0.9 | 30 | 31 | 47 | 47 | 23 | 26 | 29 | 34 | 40 |
Tampa-St. Petersburg-Clearwater | 22,121 | 1.1 | 33 | 34 | 49 | 49 | 26 | 28 | 32 | 36 | 43 |
Virginia Beach-Norfolk-Newport News | 8,893 | 0.4 | 18 | 18 | 38 | 38 | 12 | 14 | 16 | 20 | 24 |
Washington-Arlington-Alexandria | 41,199 | 2.0 | 53 | 55 | 65 | 65 | 44 | 47 | 52 | 58 | 66 |
PONE-D-20-04402
Beyond Safety Drivers: Applying air traffic control principles to support the deployment of driverless vehicles
PLOS ONE
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Reviewer #1: Very interesting paper and very good job. A nationwide remote monitoring system for driverless vehicles are proposed, which could increase safety dramatically, speed these vehicles’ deployment, and provide employment. The remote operators do not directly drive the vehicles, but provide input on high level tasks such as path-planning, object detection and classification. No more comments.
Reviewer #2: This is an interesting paper, it is well written and easy to follow. I have the following comments:
(1) on Page 8, the authors assumed arrival rate is constant. This assumption, in my opinion, is kind of over simple. If we look at the distribution of crashes (assuming that number of crashes and remote drivers needed are proportional), it is not evenly distributed by hour, day (week day vs weekend), as well as other conditions (roadway type, weather, etc.). Taking the fallen tree as an example, significant more remote drivers may be needed under adverse weathers. I encourage the authors discuss it in the paper.
(2) Lines 103 to 107 on Page 4, can you further clarify how you come up with the number of 6.25 m disengagements?
(3) Minor: first line on Page 10. “Inter-arrival and service distributions” should be “Inter-arrival and service time distributions?” missing “time”?
(4) One question not related to the work in this paper, but I am curious. How about if the remote driver makes mistakes and “causes” a crash? Should he/she take responsibility?
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Beyond Safety Drivers: Applying air traffic control principles to support the deployment of driverless vehicles
PONE-D-20-04402R1
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PONE-D-20-04402R1
Beyond Safety Drivers: Applying air traffic control principles to support the deployment of driverless vehicles
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