Input:
	Background

		Trucking Industry

In 2016, commercial trucks transported about 70 percent of all U.S.  freight, and over 250,000 heavy trucks were sold in the same year.  These trucks operate within a diverse industry that can be distinguished  in several ways:
Long-haul vs. local-haul. Long-haul trucking operations are so  named because the drivers frequently drive hundreds of miles for a  single route and can be on the road for days or weeks at a time. For  these operations, freight is usually shipped from a single customer  and may fill an entire trailer by either space or weight. Long-haul  trucking also includes “less-than-truckload” freight shipments, or  freight combined from multiple customers. In comparison, local-haul  trucking operations may involve delivering packages and shipments  between a customer and a freight company’s drop-off point, where  they are combined with other shipments in preparation to move them  over longer distances. This type of operation also includes local  cement trucks, as well as moving shipping containers at ports and  moving freight a short distance from a train that has transported it  long-distance to near its destination.
For-hire vs. private (in-house). Different types of companies—or  carriers—engage in long-haul and local trucking and are known either  as “for-hire” (those that transport goods for others) or “private” (those  that transport their own goods in their own trucks). For instance, J.B.  Hunt is a for-hire carrier that transports goods for clients, while  Walmart is a private carrier that uses its in-house fleet of trucks to  transport its own goods between its distribution centers and its  stores.
Carrier size. In addition, carriers vary in size, with fleets ranging from  one truck to tens of thousands of trucks. For example, a person might  own and drive one for-hire truck; these are known as “owner- operators.” By contrast, the largest for-hire trucking companies in the  country can have fleets of over 20,000 tractors and even more trailers.
Operating costs. Driver compensation represents either the largest  or second-largest cost component for truck carriers, depending on the  price of fuel; each typically accounts for about one-third of total  operating costs. Other operating costs include purchasing truck  tractors and trailers, as well as repair and maintenance of the trucks  and trailers, and insurance.

		Truck Drivers

BLS data indicate that in 2017, the United States had nearly 1.9 million  truck drivers categorized as “heavy and tractor-trailer truck drivers,” who  operate trucks over 26,000 pounds. This category includes many  different kinds of drivers, including long-haul and local-haul, along with  cement or garbage truck drivers and drivers of specialty loads, such as  trucks transporting cars, logs, or livestock. The number of heavy and  tractor-trailer truck drivers has increased over the last 5 years, from fewer  than 1.6 million in 2012, and is projected to increase to about 2 million  drivers by 2026. The trucking industry has also had high annual driver  turnover, according to industry reports—approaching 100 percent for  large, truckload carriers, though it can be less for small, truckload  carriers. This turnover includes drivers who move to other carriers and  others who leave the field altogether or retire. Some companies that  experience lower turnover rates are able to provide drivers with  predictable schedules and coordinate around the various obligations the  drivers may have. Firms must balance the costs of scheduling drivers to  return home more frequently with the costs of high turnover rates.
Industry reports have noted that companies find it difficult to hire and  retain sufficient numbers of long-haul drivers, even with wages reportedly  rising for many drivers. Heavy and tractor-trailer truck drivers make more  on average—$44,500 in 2017—than other types of drivers, according to  BLS data. Many drivers, including most drivers working in long-haul  trucking, are compensated on a per-mile basis rather than a per-hour  basis. The per-mile rate varies from employer to employer and may  depend on the type of cargo and the experience of the driver. Some  long-haul truck drivers are paid a share of the revenue from shipping.

		Truck Driver Training

In order to operate certain commercial vehicles, including heavy trucks  and tractor-trailers, drivers must obtain a state-issued commercial driver’s  license (CDL). DOT administers the federal CDL program through the  Federal Motor Carrier Safety Administration by setting federal standards  for knowledge and driving skills tests, among other requirements. CDL  applicants must have a state motor vehicle driver’s license and must be at  least 21 years old to operate in interstate commerce. Prior to receiving a  CDL, applicants must first pass the knowledge test and meet other  federal requirements, after which they are eligible to pursue a commercial  learner’s permit. After receiving the learner’s permit, applicants must  wait at least 14 days before taking the skills test. During this period,  applicants may train on their own with a CDL holder, with a truck driver  training school—a private school or public program run through a  community college, for example—or with a motor carrier to prepare for  the skills test. Applicants must pass all three parts of the skills test—pre- trip inspection, basic control skills, and an on-the-road driving test—in the  type of vehicle they intend to operate with their license. Apart from the  CDL requirements, some truck driving jobs (such as those that involve  handling hazardous materials) require additional endorsements, and  some employers require on-the-job training.
DOL and other federal agencies administer programs that can be used to  provide training for truck drivers. For example, DOL administers federal  employment and training programs, such as those funded through the  Workforce Innovation and Opportunity Act (WIOA), which provide training  dollars that can be used by prospective truck drivers, among others.  Likewise, the Department of Education provides federal student aid funds  that can be used at eligible accredited trucking schools, and DOT and the  Department of Veterans Affairs both operate programs that can assist  veterans interested in becoming truck drivers.

		Federal Regulation of Trucking

Federal regulation of trucking is focused primarily on interstate trucking  activity; states can have separate regulations related to intrastate motor  carriers. DOT is the lead federal agency responsible for overall vehicle  safety, including commercial truck safety. The agency also regulates  other aspects of commercial trucking, such as the maximum number of  hours truck drivers are allowed to drive. For example, under current  hours of service regulations, a truck driver may drive a maximum of 11  total hours within a 14-hour window after coming on duty. In addition,  DOT regulates CDL standards and the maximum weight of trucks allowed  on the Interstate Highway System, among other things. Until recently,  DOT’s National Highway Traffic Safety Administration led automated  vehicles policy with a focus on passenger vehicles. However, DOT’s  October 2018 federal automated vehicles policy was developed by the  Office of the Secretary of Transportation and includes several different  modes of transportation, including automated commercial trucks.

		Automated Trucks

Automated vehicles can perform certain driving tasks without human  input. They encompass diverse automated technologies ranging from  relatively simple driver assistance systems to self-driving vehicles.  Certain automated features, like adaptive cruise control, can adjust  vehicle speed in relation to other objects on the road and are currently  available on various truck models. DOT has adopted a framework for  automated driving developed by the Society of Automotive Engineers  International, which categorizes driving automation into 6 levels (see fig.  1).
Commercial trucks with Level 0 and 1 technologies, as outlined in figure  1, are already available for private ownership and are currently used on  public roadways. Level 0 encompasses conventional trucks where a  human driver controls all aspects of driving and technologies can warn  drivers of safety hazards, such as lane departure warning, but do not take  control away from the driver and are not considered automated. Level 1  technologies incorporate automatic control over one major driving  function, such as steering or speed, and examples include adaptive  cruise control and automatic emergency braking.
The Society of Automotive Engineers International categorizes vehicles  with Level 3, 4, and 5 technologies as Automated Driving Systems. At  Level 3, the system can take full control of the vehicle in certain  conditions. However, a human driver must maintain situational awareness  at all times to ensure the vehicle is functioning safely. At Level 4,  automation controls all aspects of driving in certain driving conditions and  environments, such as on highways in good weather. In these particular  driving conditions and environments, a human driver would not be  required to take over the driving task from the automated vehicle and the  system would ensure the vehicle is functioning safely. At Level 5, the  vehicle can operate fully, in any condition or environment, without a  human driver or occupant. There are various automated vehicle  technologies that could help guide a vehicle capable of driving itself,  including cameras and other sensors (see fig. 2).

	Widespread Deployment of Platooning and Self- Driving Long-Haul Trucks Is Likely Years Away, and Several Factors Will Affect Timeframes

		Platooning and Self- Driving Trucks Are Being Developed, Generally for Long-Haul Trucking

According to stakeholders we spoke with and literature we reviewed,  automated trucks, including self-driving trucks, are being developed,  generally for long-haul trucking. Specifically, we found there could be  various types of automation for long-haul trucks, including platooning,  self-driving for part of a route, and self-driving for an entire route.
Platooning. Technology developers and researchers told us there is  ongoing development and testing of truck platoons, which involve one  or more trucks following closely behind a lead truck, linked by  wireless—or vehicle-to-vehicle—communication (see fig. 3).
In a platoon, the driver in the lead truck controls the braking and  acceleration for all of the connected trucks in the platoon, while the driver  in each following truck controls its own steering. Several stakeholders  we interviewed and three studies we reviewed identified potential benefits  from platooning, including fuel savings and increased safety, for example,  due to the trucks’ faster reaction times for braking.
Self-driving for part of a route. Most of the technology developers  we spoke with said they were developing automated trucks that will be  self-driving for part of a long-haul route, such as exit-to-exit on  highways (see fig. 4).
Representatives from one developer explained that their truck uses self- driving software installed on the truck. The software instructs the truck  what to do, such as to steer or brake. In addition, cameras and other  sensors on the truck’s exterior provide the self-driving software with a  view of the truck’s surroundings to inform the software’s instructions. For  example, Light Detection and Ranging (LIDAR) sensors use lasers to  map a truck’s surroundings (see fig. 5).
Such trucks would operate with no driver intervention under favorable  conditions, such as on highways in good weather. Two developers  said that in their business models a driver would be in the truck for the  first and last portions of the route to assist with picking up and  dropping off trailers at hubs outside urban areas. Alternatively, one  developer said a remote driver—one not in the truck but operating  controls from another location—would drive the first and last portions  of a route. Stakeholders identified potential benefits of self-driving for  part of a route, such as increased safety, labor cost savings, and  addressing what they said is a truck driver shortage. Research funded  by industry also suggests that an automated truck could improve  productivity by, for example, continuing to drive to a destination while  a human in the truck conducts other work or rests. In addition, one  study noted that the most likely scenario for widespread adoption of  automated trucks is the one in which trucks are capable of self-driving  from exit-to-exit.
Self-driving for an entire route. None of the technology developers  we interviewed told us they are planning to develop automated trucks  that are self-driving for an entire route (see fig. 6).
Such trucks would be able to drive under all weather and  environmental conditions. A person would not be expected to  operate these trucks at any time. The potential benefits of these  kinds of trucks are similar to those of trucks that are self-driving for  part of a route, with higher potential labor savings because a  person would not need to drive the first and last portions of a  route.

		Widespread Deployment of Automated Trucks May Be Years to Decades Away, Depending on Technological, Operational, and Other Factors

			Anticipated Timeframes

Stakeholders we spoke with generally indicated that it will be years to  decades before the widespread deployment of automated commercial  trucks (see text box). However, many stakeholders also noted the  uncertainty of predicting a specific timeframe for particular technologies.
Platooning. Many stakeholders said that platooning will likely deploy  within the next 5 years and will be the first automated trucking  technology to be widely available. Notably, one company that is  developing platooning technology said it could begin deployment in  2019. In addition, DOT officials told us that truck platoons are  currently being tested, but that it would be difficult to estimate when  there might be widespread adoption of platooning technology.
Self-driving for part of a route. Automated trucks that are self- driving for part of a route may become available for commercial use  within the next 5 to 10 years, according to several stakeholders,  including technology developers. While such trucks may begin  appearing on roads in that timeframe, other stakeholders, including  two researchers, said widespread deployment may take more than 10  years. DOT officials noted that multiple variables make it difficult to  develop a precise estimate for the deployment and widespread  adoption of trucks that are self-driving for part of a route.
Self-driving for an entire route. Although none of the technology  developers told us they are developing trucks that would be self- driving for an entire route, other stakeholders we spoke with said such trucks could become available in more than a decade. However, most  stakeholders either did not provide a timeframe for, or said they did  not know, when such trucks might become available. Similarly, at a  listening session in August 2018, DOT officials told attendees that it  will be decades before large trucking operations replace their fleets of  conventional trucks with trucks that self-drive for an entire route.
One Stakeholder’s Description of Anticipated Timeframes for Overall Automated  Truck Adoption  One researcher described an anticipated timeframe for automated truck adoption in  which there is an initial, long period of development and testing, which would include  making technological adjustments. This period would then be followed by a period of  automated truck adoption—i.e., when such trucks replace human drivers.  At that point,  technology developers and truck manufacturers would also encounter scenarios in which  it may not be desirable to use an automated truck, such as for the transport of hazardous  materials, according to the researcher. Such scenarios would limit the extent to which  automated trucks could replace human drivers.

			Factors That May Affect Timing

Stakeholders we interviewed and the literature we examined identified  technological, operational, infrastructure, legal, and other factors that may  affect automated truck development and deployment.
Stakeholders and literature identified several technology-related  limitations that may affect the timing of automated truck deployment.  Specifically, several stakeholders and a study noted that automated  trucks may require simpler operating environments, such as highways, in  the near term because they are less complex for the technology to  navigate than roads in an urban setting, for example. Even so, a  highway presents its own challenges, several stakeholders said. For  instance, a developer, a manufacturer, and a researcher we spoke with  told us that Light Detection and Ranging (LIDAR)—a costly and complex  technology—may not be as useful at higher speeds due to its limited  range and its inability to process information about the surrounding  environment as quickly as needed at these speeds. Further, one  manufacturer told us that LIDAR is not as durable as it needs to be for  commercial trucking—for example, able to withstand dirt and debris.  Stakeholders also discussed the need to have backup systems built into  trucks’ automated systems in case of technology failures, including the  ability to guide the truck to a safe stop.
Stakeholders identified several operational factors that may pose  challenges for the deployment of automated trucks. For example, several  stakeholders said that there may be challenges with self-driving trucks  with no person inside when responding to a tire blowout or other  mechanical problems. Likewise, several stakeholders said there must be  ways for a self-driving truck to respond to required safety inspections and  communicate with inspectors. Representatives from a safety  organization noted that a truck could potentially communicate a unique  identification number through an electronic device. This number would  give the inspector information about the truck, such as safety information  from the sensors on automated trucks. Additionally, several stakeholders  said platooning may not be practical for logistical reasons, for instance, if  trucks are not traveling on the same routes or if cargo is not ready to  depart at the same time. In addition, according to stakeholders we spoke  with and literature we reviewed, the lead truck in a platoon will save less  on fuel than the following trucks. If trucking fleets adopt platooning  systems that work on commercial trucks across different companies—i.e.,  systems that are interoperable—distributing fuel savings in a manner  agreeable to all parties involved may be challenging. Representatives  from two fleet owners and one industry association we spoke with raised  concerns about platooning across different companies, including that  companies might not partner with other fleets to platoon trucks because  they would be primarily concerned with their own fuel savings, not with  saving fuel for their competitors. In addition to these operational factors,  stakeholders noted that automated trucks may be prohibitively expensive  for some smaller fleet owners, including owner-operators, particularly  when these trucks are first deployed.
Several stakeholders and relevant literature noted that certain  infrastructure factors may affect the development, testing, and  deployment of automated trucks. For example, a few stakeholders said if  one truck picks up or drops off trailers for another truck at a location near  highways, land acquisition near these highways may be an issue.  Representatives from a developer that planned to acquire land for its  business model said the land acquisition could take 5 to 10 years. The  representatives explained that they found enabling direct access to  freeways is more difficult than simply acquiring vacant land. They planned  to partner with states to create hubs on under-utilized land with existing  freeway access by, for example, repurposing abandoned rest stops. In  addition to land acquisition, two technology developers and a study  identified the need for widely available data connectivity and the related  ability to use connected vehicle technologies as an infrastructure  challenge. Connected technologies allow vehicles to communicate with  other vehicles (vehicle-to-vehicle), roadway infrastructure (vehicle-to- infrastructure), and personal communication devices. Connectivity has  potential implications for, among other things, the maps self-driving trucks  use to navigate routes and obstacles, as well as the ability for trucks in a  platoon to communicate with one another effectively. However, because  the ability for vehicles to communicate with infrastructure is not  ubiquitous, two of the developers we spoke with are not taking into  account connected infrastructure as they develop and test their  automated trucks. Two stakeholders also expressed concern about  platooning trucks and the stress they could place on bridges, for example,  that were not designed to hold the weight of two or more heavy trucks at  once. In addition, stakeholders noted that automated trucks may  encounter difficulties with things like road work or construction zones.  This may be because the truck relies on pre-built maps, in addition to  sensors, that would potentially be outdated or might not reflect current  road conditions, including any recent or temporary changes.
Several legal factors may affect the timing of development, testing, and  deployment for automated trucks, according to our stakeholder interviews  and literature review. Many stakeholders expressed concern about the  possibility of a “patchwork” of state laws related to automated trucks that  could affect interstate trucking, with some saying they would like to see a  shared national framework. For example, one technology developer said  that this emerging patchwork can make it difficult for an automated truck  to travel across the country without a driver, because some states  specifically prohibit self-driving vehicles, including trucks. However, this  same developer said that some states are less restrictive regarding the  need for a driver in a self-driving truck, and that others have ambiguous  regulations. Several stakeholders we spoke with and two studies we  reviewed noted that liability issues may arise and become more complex  for automated trucks. This may be because, for example, more parties  may become involved. One of these stakeholders—a fleet owner—said  that these parties could include the software developer, the truck  manufacturer, the owner of the truck, and, if applicable, the truck driver.  These issues could be addressed under the current liability system, and  courts would decide the various liability issues on a case-by-case basis.
In addition, several stakeholders have requested that DOT clarify whether  existing regulations require that human drivers always be present in  automated trucks, particularly those capable of Level 4 and 5 driving  automation, in which at least some of the driving is done by the  automated truck. Two technology developers have requested that DOT  confirm that regulations that apply to human drivers do not apply to  automated trucks, and one of these developers also requested  confirmation that a truck capable of at least Level 4 automation is allowed  to operate without a human on board, which could permit testing without a  person in the truck. In Preparing for the Future of Transportation:  Automated Vehicles 3.0, DOT’s automated vehicles voluntary guidance,  the agency laid out its approach to its automated vehicles policy. DOT’s  guidance stated that, going forward, DOT will interpret and, consistent  with all applicable notice and comment requirements, adapt the  definitions of “driver” and “operator” to recognize that such terms do not  refer exclusively to a human, but may include an automated system. In  the same guidance document, DOT also noted that regulations will no  longer assume that the driver of a commercial truck is always human or  that a human is necessarily present inside of a truck during its operation.
A few stakeholders also said that DOT may have to clarify the hours of  service rules if a human driver is in an automated truck that is self-driving  for part or all of a route. This is because under current hours of service  regulations, a human driver may drive a maximum of 11 total hours within  a 14-hour window after coming on duty. However, if a truck self-drives  for at least part of a route, it is unclear if a human driver would need to  comply with the existing hours of service requirements and, if not, how  the driver would account for worked time. For example, if the human  driver is not actively engaged in the driving task, whether monitoring the  automated driving system or even sleeping, there could be a question  about whether that time would be counted toward “driving,” according to  the requirements. For a list of potential legal factors identified by  stakeholders or in literature that may affect timing for the development  and deployment of automated commercial trucks, and related DOT  information, see appendix II.
Stakeholders and relevant literature identified several other factors, such  as public perception and cybersecurity, that could affect timing for the  development and deployment of automated trucks. Several stakeholders  we interviewed and a study we reviewed noted that public acceptance  concerning the safety of platooning and self-driving trucks may pose a  challenge to the deployment of these trucks. One researcher we spoke  with said interactions between truck platoons and cars may be  problematic, because drivers may need to speed in order to change lanes  around the platoons of trucks following each other closely. Similarly, other  stakeholders told us that it may be difficult for the public to accept large  automated commercial trucks. Two of these stakeholders said this is  particularly true for a heavy truck without a human driver on board— implying that vehicle size and weight play roles in the public’s acceptance  of these types of automated vehicles. Several stakeholders also  expressed concerns about cybersecurity and automated trucks’ reliance  on wireless communication and self-driving software. They said  connectivity could leave automated trucks vulnerable to cyberattacks.

	Workforce Changes Due to Automated Trucking Will Depend in Part on the Role of Future Drivers or Operators, and Will Take Time to Develop

		Workforce Effects of Automated Trucking Could Include Changes to Employment Levels, Wages, Retention, and Skills

Predicting workforce changes in light of future automated trucking is  inherently challenging, as it is based on uncertainties about how the  trucking industry will respond to new technologies that face operational,  regulatory, and other factors that could affect deployment. Many of the  stakeholders we interviewed declined to predict various possible  workforce effects, because they said to do so was too speculative.  However, stakeholders we spoke with and literature we reviewed  presented two main scenarios for the future trucking workforce: one in  which trucks would be self-driving for part of a route, without a driver or  operator, and the other in which trucks would require a driver or operator  in the truck for the entire route. An operator would monitor truck  operations and may not always function as a traditional driver. Because  most stakeholders agreed that the prospect of using fully self-driving  trucks for an entire route is either unlikely or at least several decades into  the future—and no developer we spoke with was planning to develop a  fully self-driving truck—we do not discuss the workforce effects of that  scenario in this report.

			Potential Effects If Truck Has No Driver or Operator for Part of Route

Technology developers we spoke with generally envisioned trucks that  are self-driving for part of a route, which they said would potentially lead  to significant workforce changes. Several technology developers and  researchers, along with two studies, said trucks that are self-driving for  part of a route could decrease the number of long-haul drivers, and  perhaps decrease wages and affect retention as well. Additionally, any  displaced drivers may need new skills if they change jobs, according to  several stakeholders we spoke with and studies we reviewed.
Employment levels: Technology developers we interviewed  generally predicted the number of long-haul jobs would decrease with  the adoption of trucks that are self-driving for part of a route. Drivers  constitute a significant operational cost, so part of the reported  economic rationale for self-driving trucks is to employ fewer drivers,  allowing companies to transport the same amount of freight—or  more—at lower labor costs. Several studies have analyzed the  potential number of driving jobs that might be eliminated in this  scenario, but the studies specifically noted the speculative, long-term  nature of those estimates and the inability to identify the number of  current long-haul truck drivers whose jobs could be lost sometime in  the future. Estimates in the studies we reviewed ranged from under  300,000 driver jobs lost to over 900,000 jobs lost—out of a total of  nearly 1.9 million heavy and tractor-trailer truck driver jobs, according  to BLS data—and in each case over periods of 10 to 20 years or  more.
Although long-haul jobs would decrease in this scenario, local-haul  jobs could increase and offset those losses, according to a study and  several stakeholders, including two technology developers. The  study, for example, said that automated trucking would drive long-haul  trucking costs down, leading more companies to use trucking to ship  goods. As a result, demand for trucking could increase, leading to an  increased demand for local-haul truck drivers on either end of the  long-haul routes, two studies noted.
Several stakeholders we spoke with agreed that any decrease in long- haul jobs would likely not affect many current drivers because most  will have voluntarily left driving for a different job or retired by the time  self-driving trucks are widely deployed. According to the Census  Bureau’s American Community Survey data, the average age of truck  and sales delivery drivers from 2012 through 2016 was 46. Many  stakeholders also said that trucking fleets are currently having  difficulty hiring and retaining qualified drivers, and two technology  developers said automation could help move goods in an environment  in which it is difficult to find workers.
Technology developers also told us they are focusing the initial  development of automated trucking technology in the southwest  United States because of its good weather and long highways. As a  result, any future job losses could first occur there. Additionally, BLS  data show that the estimated concentration of truck driving jobs varies  in different areas of the country (see fig. 7). One study noted that  trucking job losses in more regionally concentrated occupations are  likely to pose more challenges for workers, because more workers  with similar skills in the same labor markets will be out of work at the  same time, and thus the whole local economy will be more likely to  suffer.
Wages: If the truck is self-driving for parts of a route, wages for long- haul drivers could decrease because there would be lower demand  for—or greater supply of—such drivers, according to several  stakeholders. Moreover, one study noted that average long-haul  wages could decrease because the jobs most likely to be automated  include those that tend to be unionized and have higher wages and  benefits, such as jobs at parcel delivery companies and some private  carriers. Similarly, drivers changing occupations might face  significant wage reductions in new occupations that do not require  retraining, according to a researcher and one study. Wages for  local-haul drivers—generally lower than for long-haul drivers—could  decrease as well, because transitioning long-haul drivers could  increase competition for those jobs, according to two studies. One  technology developer presented a different perspective, saying that  wages for local-haul drivers could increase from current levels due to  increased overall demand for trucking.
Retention: Overall, retention of truck drivers could improve if the  long-haul portion of the route becomes self-driving, lessening time  drivers spend away from home—a key reason long-haul drivers leave  the profession, according to many stakeholders. However, retention  may depend on several factors, including wages, time at home, and  other working conditions, making it more difficult to predict self-driving  trucks’ effect on retention.
Skills: Long-haul drivers have skills that would transfer to local-haul  routes, so additional training may not be needed for those who move  to local-haul routes. However, displaced long-haul drivers seeking to  move to a different occupation or industry may need additional  training, according to several stakeholders and two studies. From  2012-2016, the highest level of education attainment for almost 65  percent of truck and sales delivery drivers was high school or its  equivalent.

			Potential Effects If Driver or Operator Remains in Truck

Most officials from truck driver training schools, organizations  representing truck drivers, and workforce development boards envisioned  automated trucks as continuing to need either a driver or some kind of  operator in the truck, with several noting that drivers may need to do non- driving tasks. Automated trucking with an operator in the truck would have  a more limited effect on the numbers of truck drivers, but would still result  in workforce changes, according to several stakeholders. As with the  driverless scenario, many stakeholders said future developments were so  uncertain that they could not predict how automated trucking would affect  various aspects of the workforce, such as wages or retention.
Employment levels: Under this scenario, automated trucking would  have a more limited effect on employment levels. Several  stakeholders noted, for example, that a person would still be needed  in the truck to manage emergencies, repair flat tires, and secure  cargo, among other duties. (See text box.) For example, one study  noted that even for trucking jobs identified as the most likely to be  automated, driving may represent only about half of drivers’ total work  time. Additionally, particular kinds of long-haul trucking may present  different non-driving tasks that could make automating those driving  jobs more difficult.
Wages: If the truck has an operator, several stakeholders said that  wages might increase if increased skills are needed to operate more  sophisticated equipment. However, several other stakeholders said  wages might not change significantly or could decrease with fewer  driving tasks. Two studies noted that wage changes were difficult to  predict and could be affected by specific policy interventions.
Truck Drivers: Responsible for More than Just Driving   Truck drivers have many responsibilities other than driving a truck. Non-driving tasks for  heavy and tractor-trailer truck drivers can include:  checking vehicles to ensure that mechanical, safety, and emergency equipment is in  good working order;  loading or unloading trucks, including checking contents for any damage;  inspecting loads to ensure that cargo is secure; and  performing basic vehicle maintenance tasks, such as adding fuel or radiator fluid;  performing minor repairs; or removing debris from loaded trailers.
Retention: Many stakeholders said new technology could help the  trucking industry bring in and retain more people—such as women  and younger workers—if it could, for example, make truck driving  safer, less stressful, and less physically demanding. Others  cautioned that automated technology may not decrease truck  operators’ time away from home, because they would still have to be  in the truck for the entirety of long-haul routes. One stakeholder, who  was also a truck driver, said that many truck drivers enjoy driving, so  automating aspects of that task would not necessarily entice those  drivers to stay in the job. Two other stakeholders noted that some  drivers may not want to learn how the new technology works and  could leave the field rather than drive automated trucks.
Skills: Future truck operators may need new skills to work with  automated technology that assists rather than replaces them, many  stakeholders noted. For example, operators may need to adapt to  technology that takes over a number of the standard driving functions,  such as braking, staying in a designated lane, and keeping a safe  distance from other vehicles. Operators may also need to understand  how to monitor software and hardware used to automate the driving  function and how to make appropriate use of advanced safety  systems. Furthermore, officials from many truck driver training schools  and workforce development boards said additional certification  beyond the standard CDL may be needed in order to demonstrate an  understanding of how to operate the technology in automated trucks.  In some instances, the skills needed may vary across trucking  companies and trucks, requiring further on-the-job training.

			New Trucking-Related Jobs

Regardless of their vision for how automated trucking might materialize,  many stakeholders said there could be new trucking-related occupations,  such as specialized technicians, mechanics, and engineers, which will  accompany the deployment of automated trucks. For example, one study  noted that these jobs could include producing the technology used by  automated trucks, in addition to jobs created as a result of potential  greater spending on other consumer goods and services, in the event that  automated trucking decreases overall industry transportation costs.  Another study noted that autonomous trucks, e-commerce, and economic  growth are together poised to create many new trucking jobs. However,  new jobs may be located in different geographical areas than any jobs  lost, and as noted above, may require different skills than the prior jobs.  One study noted this development could potentially leave lower-skilled  workers competing for jobs that pay little and have few opportunities for  advancement.

		Stakeholders Said the Anticipated Timeframe for Automated Trucking’s Effects on the Workforce Provides an Opportunity for a Federal Response

While many stakeholders we spoke with and several studies we reviewed  stated that the potential workforce effects of automated trucking were  difficult to predict, they generally agreed that any effect would not occur  for at least 5 to 10 years. Several stakeholders and two studies said this  time horizon provides an opportunity for federal agencies and workers to  prepare for potential workforce changes. One of these studies noted that  trucking policy is complex; any changes could take a long time to fully  materialize. That same study suggested that now is the appropriate time  for policy research and debate. The other study and several stakeholders  stated that potential workforce effects are not set in stone, and that public  policy could influence specific workforce outcomes. That study said that  with advance planning, the federal government and other stakeholders  could realize the possible benefits of automated trucks and other vehicles  while mitigating potential workforce effects and other costs.

	DOT and DOL Could Take Additional Steps to Fully Consider Automated Trucking’s Potential Workforce Effects, as Technology Evolves

		DOT Has Gathered Stakeholder Perspectives to Inform Potential Regulatory Changes, and DOL Has Incorporated Technology Changes into Employment Projections

DOT and DOL have both taken some steps to prepare for the potential  workforce effects of automated trucking. DOT has held events to obtain  stakeholder perspectives on automated vehicles policy, including how it  affects commercial long-haul trucks. For example, DOT had public  listening sessions in 2017 and 2018 to solicit information on the design,  development, testing, and integration of Automated Driving Systems, and  requests for comment to inform potential rulemaking efforts for the  Federal Motor Carrier Safety Regulations. DOT officials said their role  during these discussions was to hear stakeholder concerns. They also  said that their ongoing goal is to identify barriers in their regulations to  safe deployment of automated driving technology. Stakeholders have  raised concerns about the potential workforce effects of automated trucks  at DOT’s listening sessions. For example, after participants questioned  potential job losses at a listening session in August 2018, DOT officials  said that automation may eventually change the role of a truck driver from  driver to technician and that any changes would probably not be  immediate. DOL officials said they have participated in some of DOT’s  listening sessions.
For its part, DOL has taken steps to study how automated trucking may  affect the near-term demand for truck drivers as part of their standard,  biennial employment projections for all occupations. DOL officials said they consulted experts and economic studies prior to publishing their  most recent projections, covering 2016 to 2026, and included information  on possible effects of automation in projections for heavy and tractor- trailer truck drivers. The projections state that the demand for these  drivers is expected to grow by 5.8 percent between 2016 and 2026, with  an average of over 200,000 job openings each year, of which 10,000 are  projected to be new jobs. DOL’s analysis anticipated that automation  will not reduce the number of drivers by 2026. DOL officials said that  they expect automation to assist drivers rather than displace them in the  near term. Unlike estimates developed by other researchers, these  numbers do not include potential job losses after 2026, though DOL  officials noted that the agency’s next projections, for 2018 to 2028, will  incorporate information on how automated trucking technology has  evolved since the 2016-2026 projections. Additionally, officials said the  agency is transitioning to annual updates of projections to more quickly  incorporate developing information.
Congress has directed DOT to consult with DOL to study the workforce  impacts of automated trucking technology. Specifically, the Explanatory  Statement accompanying the Consolidated Appropriations Act, 2018  instructs the Secretary of Transportation to consult with the Secretary of  Labor to conduct a comprehensive analysis of the effect of advanced  driver-assistance systems and highly automated vehicle technology on  drivers and operators of commercial vehicles, including commercial  trucks. Congress directed DOT to include stakeholder outreach in its  analysis and provide information on workers who may be displaced as a  result of such technology, as well as minimum and recommended training  requirements for operating vehicles with these systems. DOL officials told  us that they have begun collaborating with DOT on this study by  consulting with organized labor and other stakeholders. In October 2018,  DOT issued a request for information to solicit comments on the scope of  this analysis and detailed several potential research questions, including  which commercial drivers are likely to be affected and what skills might be  needed to operate new vehicles or transition to new jobs. DOT also  announced that it is planning to coordinate with the Departments of  Commerce and Health and Human Services, in addition to consulting with  DOL to conduct this analysis. The Explanatory Statement directs DOT to  conduct this analysis by March 23, 2019, and DOT officials told us they  expect to meet this deadline and report on the analysis by that date.

		DOL and DOT Do Not Have Plans to Gather and Share Information about the Potential Workforce Effects of Automated Trucking as Technology Evolves

			Convening Key Groups of Stakeholders on an Ongoing Basis to Gather Information

DOL and DOT have taken some steps to convene stakeholders to inform  DOT’s analysis of automated trucking in advance of March 2019.  However, DOL and DOT have not made plans to continue collaborating to  convene key groups of stakeholders as the technology evolves to gather  information about potential workforce effects of automated trucking.  Insofar as automated trucking technology is still evolving, convening  stakeholders solely to inform the March 2019 analysis will not provide  agency officials with sufficient information about important developments  that may occur after the analysis is completed. This analysis will be an  important step. However, DOT must complete it before potential  workforce effects can be more fully predicted. After its completion,  developers will likely continue to test their technologies, and issues  related to operational and other factors that will affect the deployment of  automated trucks may change or be resolved. For the agencies to more  fully understand these developments and clarify the range of associated  workforce effects, they would need to collaborate and to continue to  gather information in the future, for example by continuing to convene key  groups of stakeholders as the technology evolves. The majority of  stakeholders we spoke with, including representatives from local  workforce development boards, truck driver training schools, technology  developers, and groups representing truck drivers, told us it would be  helpful for federal agencies to play a convening role so that DOL and  DOT can better anticipate and understand any potential workforce  changes. Several stakeholders also said that convening stakeholders  would enable DOL and DOT to surface different parties’ concerns.  Additionally, our recent report on emerging technologies found that  federal agencies can play an important role in convening stakeholders to  gather information in areas where technology is still under development,  including information on the research plans of industry stakeholders and  ways to address national needs.
Continuing to convene stakeholders could also help agencies to identify  any information or data gaps that may need to be addressed to  understand the potential workforce effects of automated trucking. DOL  officials said that because the technology is still advancing, the related  workforce effects, including the magnitude of any job losses, are  uncertain. They also said they do not have information to identify the  number of long-haul truck drivers, whose jobs may be the most likely to  be affected by automation. Specifically, the occupational code DOL uses  to classify heavy and tractor-trailer truck drivers captures drivers who  operate any type of heavy truck. Along with long-haul drivers, this code  includes other drivers whose jobs may be harder to automate, such as  tow truck operators. Experts who participated in the National Science  Foundation-sponsored workshop on the potential workforce effects of  automated trucking also identified information gaps. They noted that  more information is needed in several areas, including a better  understanding of current truck drivers’ skills beyond driving, how those  skills might translate to other occupational areas, and new jobs and skills  that will be required with the deployment of automated trucks. DOL  officials said that the agency provides information on knowledge, skills,  and abilities for various driver occupations, as well as detailed work  activities, on its Occupational Information Network (O*NET). However,  that information is based on surveys to current workers and therefore  does not include what skills future drivers may need as automated  technology evolves.
DOL officials told us they do not typically convene stakeholders on an  industry-specific basis. They also said that state and local workforce  development boards are best positioned to identify and respond to  changes in their local economy and employment needs, because these  boards include members from the local business community who know  which industries are growing in their local labor markets. However, there  are close to 1.9 million heavy and tractor-trailer truck drivers across the  country, making the trucking industry an important segment of the  national workforce. In addition, one of DOL’s objectives in its fiscal year  2018-2022 strategic plan is to provide timely, accurate, and relevant  information on labor market activity, working conditions, and price  changes. While DOL officials said they consider the agency’s national  labor statistics as the primary tool in understanding macroeconomic  changes, they acknowledged that gathering information from local boards  and other stakeholders may complement those statistics. DOL officials  said they may consider continuing to convene stakeholders to learn more  about automated trucking if they find that their current efforts with DOT  provide fruitful information, but they currently do not have plans to do so.  If DOL waits until the effects of automated trucking on the workforce are  widespread enough to affect multiple local economies, the agency will  have missed the opportunity to proactively gather information that could  help it anticipate large-scale workforce changes in this important industry  before they take effect.
DOT officials told us they have likewise not made plans to work with DOL  to convene stakeholders on an ongoing basis to gather information.  Rather, they said they have concentrated on developing the analysis  described by the Explanatory Statement accompanying the Consolidated  Appropriations Act, 2018 and they do not plan to update that analysis  after it is completed. Nonetheless, one of the objectives outlined by DOT  in its fiscal year 2018-2022 strategic plan is to promote economic  competitiveness by supporting the development of appropriately skilled  transportation workers (including truck drivers who transport freight) and  strategies to meet emerging workforce challenges. Working with DOL to  gather and analyze information from stakeholders as technology  continues to develop could assist DOT in meeting this goal. DOT has  previously collaborated with DOL on transportation workforce issues. For  example, in 2015, DOT and DOL worked with the Department of  Education on a blueprint for aligning investments in transportation,  including trucking, with career pathways. The report highlighted  potential future growth areas in the transportation industry and identified  potential jobs that may be in demand through 2022. Unless DOL and  DOT continue to gather information from stakeholders as automated  trucking technology evolves, they may be unable to fully anticipate the  emerging workforce challenges that may result. DOT’s prior efforts to  convene stakeholders to address automated vehicles could serve as a  model for gathering information from stakeholders about automated  trucking. For example, DOT held a series of meetings across the country  to gather information, identify key issues, and support the transportation  community to integrate automated vehicles onto roads for its National  Dialogue on Highway Automation. Further, analyzing information from  ongoing meetings with stakeholders could help DOT as it considers  potential workforce-related regulatory changes that might be affected by  automated truck technologies, such as the requirements to obtain a  commercial driver’s license or the maximum number of hours commercial  truck drivers are permitted to work.

			Sharing Information

DOL has not provided information to stakeholders about the potential  workforce effects of automated trucking technology, including how the  skills needed to operate a truck may change in the future. DOL officials  told us they have not done so, in part, because they do not yet know how  skills and training needed to be a truck driver might change, if at all.  Representatives from all of the truck driver training schools and training  associations we interviewed said they expect drivers to need new skills to  operate or maintain automated trucks, and that future truck drivers may  need an additional certification or endorsement to their commercial driver’s license. However, in the absence of specific information about  future skill changes, they all said they did not know what specific  adjustments would be needed to their curriculum. Additionally, nearly all  stakeholders we spoke with—including representatives of technology  developers, truck driver training schools, and local workforce  development boards—told us that federal agencies can help prepare the  future workforce by sharing information with stakeholders about  impending workforce changes. In particular, some workforce officials we  spoke with said they would benefit from information about technology  developers’ plans that would affect future demand or skills for truck  drivers.
Furthermore, DOL officials told us that heavy and tractor-trailer truck  driving was the most common type of occupational training funded  through the WIOA Adult and Dislocated Worker programs between April  2017 and March 2018, the most recent period for which data are  available. Specifically, local workforce development boards provided  funding from these programs to roughly 17,000 individuals for heavy and  tractor-trailer truck driver training during that year, or about 15 percent of  all individuals who received training services that began within that  timeframe. This was more than twice as many individuals as those who  received funding for nursing assistant training, the second most frequently  funded type of training through these programs.
As previously noted, one of DOL’s strategic objectives is to provide timely  and accurate labor market information. In addition, according to  Standards for Internal Control in the Federal Government, an agency’s  management should externally communicate the necessary quality  information to achieve the entity’s objective. This includes  communicating quality information so that external parties can help the  entity address related risks. Additionally, our work has shown that federal  agencies can play an important role in sharing information. We have  noted that such information sharing is important to help maintain U.S.  competiveness. DOT’s strategic plan highlights the agency’s concern  that the lack of credentialed workers, combined with projected  retirements, threaten to cause significant worker shortages, and that the  introduction of innovations and new technologies adds additional  complexity for workforce development. Consulting with DOT to provide stakeholders with information about how automated technology could  affect the number of trucking jobs and the skills needed to drive or  operate commercial trucks would better position local workforce  development boards, truck driver training schools, and others to  adequately prepare the workforce for future needs.

			Responding to Potential Job Losses

DOL officials said that existing employment and training programs  administered by the agency, usually through grants, are generally  designed to respond to economic changes that may result in job losses,  including any that may result from automated trucking. In addition, DOL  officials said that the agency has several resources to support state and  local workforce areas to respond to mass layoffs and help workers  upgrade their skills. For example, Rapid Response, which is carried out  by states and local workforce development agencies, can provide  services to employees after a layoff, including career counseling, job  search assistance, and information about unemployment insurance and  training opportunities. Additionally, under WIOA, local workforce  development boards can use up to 20 percent of their Adult and  Dislocated Worker allocations to help fund the cost of providing  incumbent worker training designed to help avert potential layoffs or  increase the skill levels of employees. While these programs may help  mitigate any future job losses due to automated trucking, DOL would be  better positioned to help local economies leverage them effectively if the  agency continued to convene stakeholders, building on its efforts to  gather and share good information on when and how those workforce  effects are likely to materialize as technology evolves.

	Conclusions

Automated and self-driving technology for commercial trucks could make  the industry safer and more efficient, but it also introduces significant  uncertainties for the trucking workforce that DOL and DOT, in  consultation with other federal agencies and stakeholders, can help  navigate. For example, there is uncertainty about the widespread  deployment of self-driving trucks as well as what the resulting effects will  be on employment levels, wages, and needed skills. Although technology  companies generally envision self-driving trucks being used for long-haul  routes—which could result in fewer long-haul trucking jobs—other  stakeholders argued that a truck will always need a driver or operator.  Stakeholders we interviewed also lacked consensus about what  automated trucking might mean for wages and what new skills will be  needed to drive or operate automated trucks.
Federal agencies have an opportunity to prepare truck drivers for the  possible workforce effects of automated trucking. Many stakeholders  noted that the effects would be gradual, giving the government time to  act, but studies note the effects could eventually be significant, possibly  affecting hundreds of thousands of truck driving jobs.
DOT is taking an important step toward learning about these workforce  effects by consulting with DOL and other stakeholders to inform DOT’s  analysis of these developments. However, these agencies have not made  plans to continue to convene stakeholders to gather information on an  ongoing basis or update their analysis as the technology evolves and the  effects become more apparent. Doing so could allow DOL and DOT the  foresight to consider whether additional policy changes are needed to  prepare for any possible future workforce effects. Similarly, DOL’s  publication of routine employment projections and current driver skills and  tasks provide useful information. However, DOL has not shared  information on what skills drivers might require in the future with other key  stakeholders, including technology developers, industry experts, truck  driver representatives, training schools, local workforce development  boards, and other relevant federal agencies. As a result, those  stakeholders may miss an opportunity to better anticipate and plan for  changes that may arise from automated trucking technology, including  potential labor displacement, wage changes, and the need for new skills.

	Recommendations for Executive Action

We are making the following four recommendations, including two for the  Department of Labor and two for the Department of Transportation:  1.  The Secretary of Labor should collaborate with the Secretary of  Transportation to continue to convene key groups of stakeholders to  gather information on potential workforce changes that may result  from automated trucking as the technology evolves, including  analyzing needed skills and identifying any information or data gaps,  to allow the agencies to fully consider how to respond to any changes.  These stakeholders could include, for example, representatives of  other relevant federal agencies, technology developers, the trucking  industry, organizations that represent truck drivers, truck driver  training schools, state workforce agencies, and local workforce  development boards. (Recommendation 1)  2.  The Secretary of Transportation should collaborate with the Secretary  of Labor to continue to convene key groups of stakeholders to gather  information on potential workforce changes that may result from  automated trucking as the technology evolves, including analyzing  needed skills and identifying any information or data gaps, to allow the  agencies to fully consider how to respond to any changes. These  stakeholders could include, for example, representatives of other  relevant federal agencies, technology developers, the trucking  industry, organizations that represent truck drivers, truck driver  training schools, state workforce agencies, and local workforce  development boards. (Recommendation 2)  3.  The Secretary of Transportation should consult with the Secretary of  Labor to further analyze the potential effects of automated trucking  technology on drivers to inform potential workforce-related regulatory  changes, such as the requirements to obtain a commercial driver’s  license or hours of service requirements (e.g., the maximum hours  commercial truck drivers are permitted to work). This could include  leveraging the analysis described by the Explanatory Statement  accompanying the Consolidated Appropriations Act, 2018 once it is  complete, as well as information the department obtains from  stakeholders as the technology evolves. (Recommendation 3)  4.  The Secretary of Labor should consult with the Secretary of  Transportation to share information with key stakeholders on the  potential effects of automated trucking on the workforce as the  technology evolves. These stakeholders could include, for example,  representatives of other relevant federal agencies, technology  developers, the trucking industry, organizations that represent truck  drivers, truck driver training schools, state workforce agencies, and  local workforce development boards. (Recommendation 4)

	Agency Comments and Our Evaluation

We provided a draft of this report for review and comment to the  Departments of Education, Labor (DOL), Transportation (DOT), and  Veterans Affairs. We received formal written comments from DOL and  DOT, which are reproduced in appendices III and IV, respectively. In  addition, DOL and DOT provided technical comments, which we have  incorporated as appropriate. The Departments of Education and Veterans  Affairs did not have comments on our report.
In its written comments, DOL agreed with our recommendations and  noted several efforts that it said will help the agency assess and provide  information on the potential workforce effects of evolving technologies,  such as automated trucking. For example, DOL noted that the agency’s  employment projections incorporate expert interviews and other  information to identify shifts in industry employment. DOL is also currently  consulting with DOT to study these workforce effects, and agreed to  consider what other information and stakeholder meetings remain  necessary after that study—due in March 2019—is completed. Likewise,  DOL agreed to share related information as the technology evolves, and  the agency noted it currently publishes employment projections and other  occupational information. While useful, these efforts alone will not allow  DOL to sufficiently anticipate the future workforce effects of automated  trucking. For instance, the broad employment projections do not provide  estimates specifically for the long-haul truck drivers who could be affected  by automated trucking first. Further, DOL’s occupational information is  based on surveys of current workers, so it does not include the skills  future drivers will need as automated trucking evolves. Therefore, we  continue to believe that convening stakeholders and sharing information  about potential workforce effects in the future will position DOL to better  understand and inform key stakeholders of these changes.
In its written comments, DOT agreed with our recommendations. DOT  noted two of its current efforts related to automated trucking technology,  namely its October 2018 automated vehicles voluntary guidance,  Preparing for the Future of Transportation: Automated Vehicles 3.0, and  its forthcoming Congressionally-directed research on the impact of  automated vehicle technologies on the workforce.
We are sending copies of this report to the appropriate congressional  committees, the Secretaries of Education, Labor, Transportation, and  Veterans Affairs, and other interested parties. In addition, the report will  be available at no charge on the GAO website at http://www.gao.gov.
If you or your staff have any questions concerning this report, please  contact us at (202) 512-7215 or brownbarnesc@gao.gov or  flemings@gao.gov. Contact points for our Offices of Congressional  Relations and Public Affairs may be found on the last page of this report.  GAO staff who made key contributions to this report are listed in appendix V.

Appendix I: Objectives, Scope, and Methodology

Our objectives were to examine: (1) what is known about how and when  automated vehicle technologies could affect commercial trucks; (2) what  is known about how the adoption of automated trucks could affect the  commercial trucking workforce; and (3) the extent to which the  Department of Transportation (DOT) and Department of Labor (DOL) are  preparing to assist drivers whose jobs may be affected by automated  trucking.
For all the objectives, we reviewed relevant federal laws and regulations  as well documentation from DOT and DOL. To determine the extent to  which federal agencies are preparing to assist current and future drivers,  we compared DOT and DOL’s efforts against their strategic plans as well  as Standards for Internal Control in the Federal Government.  Additionally, we:
Conducted Interviews: We interviewed officials from several federal  agencies to obtain relevant information about our objectives, including  the Departments of Education, Labor, Transportation, and Veterans  Affairs, as well as the National Science Foundation.
To obtain information about all of our objectives, we also interviewed  other selected stakeholders. We used our initial research and  interviews to develop a list of stakeholder categories that would  provide informed perspectives, which when taken as a whole,  provided a balanced perspective to answer our objectives. We  selected stakeholders who had a range of perspectives regarding the  timing for adoption of automated trucking technology, and how this  adoption could affect the truck driving workforce. We used the  following criteria to select interviewees:  1.  authored a report, article, book, or paper regarding automated  trucking technology or its potential workforce effects;  2.  participated in panels, hearings, or roundtables regarding automated  trucking or its potential workforce effects; or  3.  was recommended by at least one of our interviewees.
We interviewed organized labor representatives; researchers; and  representatives from three truck manufacturers and three companies  operating their own trucking fleet; two national industry organizations; one  national safety organization; four truck driver training schools; an  association of state and local workforce organizations; and four local  workforce development boards. We selected the schools in part based on  recommendations from an association of truck driver training schools, and  included two accredited and two non-accredited schools in our selection.  We selected three of the workforce development boards due to the  prevalence of trucking jobs in their areas and the other board because it  was in an area that several stakeholders suggested could be early to  adopt automated trucking technology.
Additionally, we visited California, where we interviewed representatives  of four automated truck technology developers and a manufacturer, and  viewed demonstrations of automated trucking technology. We selected  California because it had the largest number of technology developers  that we identified through our research efforts.
We asked all of these stakeholders a core set of questions, as well as  tailored questions based on their expertise. Some of the questions we  asked stakeholders varied, and some stakeholders chose not to answer  every question we asked because they either did not think they had  sufficient knowledge about the specific question or did not want to make  predictions about future industry developments. Therefore, we generally  did not report the specific number of stakeholder responses in this report.  The views of the stakeholders we interviewed are illustrative examples  and may not be generalizable. For a full list of stakeholders we  interviewed, see table 1.
Analyzed federal data. To examine how the adoption of automated  trucks could affect the current and future trucking workforce, we  analyzed relevant data from the Bureau of Labor Statistics (BLS) and  the Census Bureau on the current trucking workforce. Specifically, we  examined BLS’s Occupational Employment Statistics to obtain  employment level and wage data for heavy and tractor-trailer truck  drivers (Standard Occupational Classification code 53-3032). The  Occupational Employment Statistics survey is a federal-state  cooperative program between the Bureau of Labor Statistics and  State Workforce Agencies. The survey provides estimates regarding  occupational employment and wage rates for the nation as a whole,  by state, by metropolitan or nonmetropolitan area, and by industry or  ownership. Data from self-employed persons are not included in the  estimates. For our analysis of geographic concentration of heavy and  tractor-trailer truck driving jobs, we carried out a one-sided test at the  0.05 percent level of significance of the null hypothesis that a region’s  concentration is equal to or less than twice the national concentration  versus the alternative hypothesis, that the region’s concentration is  greater than twice the national concentration. We classified the  results, excluding any unreliable areas (i.e., areas with a 95 percent  confidence level margin of error for the estimated number of truck  drivers that was larger than 30 percent of the estimate itself). We used  Poisson tests because these are more appropriate for event  occurrences in smaller populations or on a small number of cases. In  addition, we analyzed data from the Census Bureau’s American  Community Survey regarding the education level, sex, and age of  current truck drivers and other drivers. The American Community  Survey is an ongoing survey that collects information about the U.S.  population such as jobs and occupations, educational attainment,  income and earnings and other topics. According to the Census  Bureau’s description of the American Community Survey, this survey  uses a series of monthly samples to produce annually updated  estimates for the same small areas (census tracts and block groups)  formerly surveyed via the decennial census long-form sample. Based  on our review of related documents and interviews with  knowledgeable agency officials, we found the data to be reliable for  our purposes.
Synthesized literature. To explore how and when automated vehicle  technologies could affect the current fleet of commercial trucks and  gather information about the possible employment effects of this  technology, we conducted a review of key research related to  automated vehicle technologies for commercial trucks. We searched  bibliographic databases for articles that were published between  January 1, 2014 and May 22, 2018 and included key terms such as  “autonomous”, “automated”, “driverless”, and “truck platoon” to  describe the trucking technology. We also asked the researchers we  interviewed to identify any studies that may be relevant to our work.
Our search initially resulted in over 250 articles with potential  relevance to our objectives. Two analysts reviewed the abstracts of  these articles to determine if the articles in this initial search were  germane to our objectives. We excluded any articles that were not  relevant to our objectives or did not meet our standards for empirical  analysis.
We included articles that were published in peer review journals, by  industry, or by government agencies, as well as articles that were  recommended by researchers we interviewed. We identified a final list  of 12 studies that met our criteria. Although we reviewed each study’s  methodological approach, we did not independently assess the  evidence in the articles or verify the analysis of the evidence that was  used to come to the conclusions these studies reached.

Appendix II: Potential Legal Factors That May Affect Timing of Automated Trucking

Appendix III: Comments from the Department of Labor

Appendix IV: Comments from the Department of Transportation

Appendix V: GAO Contacts and Staff Acknowledgments

	GAO Contacts

Cindy Brown Barnes or Susan Fleming, (202) 512-7215 or  brownbarnesc@gao.gov or flemings@gao.gov.

	Staff Acknowledgments

GAO staff who made major contributions to this report include Brandon  Haller (Assistant Director), Rebecca Woiwode (Assistant Director), Drew  Nelson (Analyst-in-Charge), MacKenzie Cooper, Marcia Fernandez, and  Hedieh Fusfield. Additional assistance was provided by Susan Aschoff,  David Ballard, James Bennett, Melinda Cordero, Patricia Donahue, Philip  Farah, Camilo Flores Monckeberg, David Hooper, Angie Jacobs, Michael  Kniss, Terence Lam, Ethan Levy, Sheila R. McCoy, Madhav Panwar,  James Rebbe, Benjamin Sinoff, Pamela Snedden, Almeta Spencer, John  Stambaugh, Walter Vance, Sonya Vartivarian, and Stephen C. Yoder.

Output:
Why GAO Did This Study

Automated vehicle technology may eventually make commercial trucking more efficient and safer, but also has the potential to change the employment landscape for nearly 1.9 million heavy and tractor-trailer truck drivers, among others. GAO was asked to examine the potential workforce effects of automated trucking.
This report addresses (1) what is known about how and when automated vehicle technologies could affect commercial trucks; (2) what is known about how the adoption of automated trucks could affect the commercial trucking workforce; and (3) the extent to which DOT and DOL are preparing to assist drivers whose jobs may be affected. GAO reviewed research since 2014 on automated trucking technology, viewed demonstrations of this technology, and analyzed federal data on the truck driver workforce. GAO also interviewed officials from DOT and DOL, as well as a range of stakeholders, including technology developers, companies operating their own trucking fleets, truck driver training schools, truck driver associations, and workforce development boards.

What GAO Found

Automated trucks, including self-driving trucks, are being developed for long-haul trucking operations, but widespread commercial deployment is likely years or decades away, according to stakeholders. Most technology developers said they were developing trucks that can travel without drivers for part of a route, and some stakeholders said such trucks may become available within 5 to 10 years. Various technologies, including sensors and cameras, could help guide a truck capable of driving itself (see figure). However, the adoption of this technology depends on factors such as technological limitations and public acceptance.
Stakeholders GAO interviewed predicted two main scenarios for how the adoption of automated trucks could affect the trucking workforce, which varied depending on the future role of drivers or operators. Technology developers, among others, described one scenario in which self-driving trucks are used on highway portions of long-haul trips. Stakeholders noted this scenario would likely reduce the number of long-haul truck drivers needed and could decrease wages because of lower demand for such drivers. In contrast, groups representing truck drivers, among others, predicted a scenario in which a truck would have an operator at all times for complex driving and other non-driving tasks, and the number of drivers or operators would not change as significantly. However, stakeholders lacked consensus on the potential effect this scenario might have on wages and driver retention. Most stakeholders said automated trucking could create new jobs, and that any workforce effects would take time—providing an opportunity for a federal response, such as any needed policy changes.
The Department of Transportation (DOT) is consulting with the Department of Labor (DOL) to conduct a congressionally-directed analysis of the workforce impacts of automated trucking by March 2019. As part of this analysis, DOT and DOL have coordinated to conduct stakeholder outreach. However, they do not currently plan to convene stakeholders on a regular basis to gather information because they have focused on completing this analysis first. Continuing to convene stakeholders could provide the agencies foresight about policy changes that may be needed to prepare for any workforce effects as this technology evolves.

What GAO Recommends

GAO is making four recommendations, including that both DOT and DOL should continue to convene key stakeholders as the automated trucking technology evolves to help the agencies analyze and respond to potential workforce changes that may result. DOT and DOL agreed with the recommendations.