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Suggested Citation:"9 Autonomous Vehicles." National Academies of Sciences, Engineering, and Medicine. 2021. Assessment of Technologies for Improving Light-Duty Vehicle Fuel Economy—2025-2035. Washington, DC: The National Academies Press. doi: 10.17226/26092.
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9

Autonomous Vehicles

9.1 INTRODUCTION

Self-driving vehicles have been a frequent topic in automotive news articles and auto executive talks around the world for more than a decade. They have captured the interest of investors and suppliers small and large and have become a strong motive force behind many start-ups. The high level of automaker and supplier investments, mergers and acquisitions, and active programs in this area, including several automaker announcements of production of fully autonomous vehicles in the near future, led to a widespread belief that cars that drive themselves would soon be commonplace. Level 4 automated vehicles, which operate without human engagement in specified areas or modes, are already in revenue service by fleets in Arizona, Texas, and Florida (Bloomberg, 2020). More recently, however, many have questioned the readiness of the technology for volume commercialization.

Meanwhile parties focused on climate stability, urban livability, and transportation equity have begun to weigh in on the role of autonomous vehicles in a changing mobility landscape. They have raised concerns, independent of the technical challenges to deployment, that autonomous driving could aggravate many of the problems arising from a transportation system highly dependent upon inexpensive travel in personal vehicles. Hence, while the arrival of autonomous vehicles seems inevitable, there is considerable uncertainty regarding the timing and consequences of their arrival.

Chapter 8 discusses ways in which connected and automated vehicle (CAV) technologies can affect fuel efficiency. This chapter is about fully autonomous vehicles—that is, Level 4 and Level 5 CAVs. It is concerned with the energy implications not only of the properties of the vehicles themselves but also of changes in vehicle ownership, travel choices, and driving modes that would result from the use of vehicles that drive themselves. Fully autonomous vehicles would introduce qualitative changes in vehicle use and could allow wholesale transformation of the transportation system and travel behavior. People previously unable to drive will have the ability to travel by car unassisted. Some people currently owning and driving their own cars may choose to share rides in autonomous fleet vehicles instead, if mobility services make it easy and cheap to do so. Others will choose to own an autonomous vehicle and may drive more miles as a result, because they can reclaim travel time for other purposes. In that case commuting distances could be expected to increase as some people choose to live farther from their places of work and other common trip destinations. As these technologies are deployed, the system may operate with autonomous vehicles typically carrying multiple passengers in urban areas or, alternatively, with autonomous vehicles driving long distances with no passengers at all, depending on cost and convenience.

Suggested Citation:"9 Autonomous Vehicles." National Academies of Sciences, Engineering, and Medicine. 2021. Assessment of Technologies for Improving Light-Duty Vehicle Fuel Economy—2025-2035. Washington, DC: The National Academies Press. doi: 10.17226/26092.
×

Possible impacts on transit, walking, and biking vary widely as well: autonomous vehicles could divert trips from other modes or complement them, for example by filling transit service gaps with ride hailing options made more affordable by not requiring a driver.

Autonomous vehicles could also be used as public transit vehicles and shuttles, with savings from reduced labor costs facilitating expansions in service and perhaps more comprehensive coverage with on-demand, flexible route services. Another likely early application of this technology is urban delivery vehicles. The rise of e-commerce and growth in consumption of prepared food have increased the demand for home delivery and the cost of providing it would be greatly diminished without the cost of drivers. In all these applications, autonomous vehicle deployment raises concerns about loss in driver jobs, which could serve as a barrier to acceptance. Such services will create new jobs in fulfillment and logistics, however, so net job impacts are unclear. The COVID-19 pandemic highlights other dynamics in the prospects for autonomous vehicles: while the pandemic has greatly increased demand for home delivery, it has also reduced demand for ride hailing and public transit service. The long-term implications of these developments remain to be seen.

Interest in autonomous vehicles for personal use is based on prospects for expanded mobility and greater convenience. These vehicles could fundamentally alter how people choose to travel and how transportation systems are designed, however, as well as affect levels of congestion, the number of miles driven, and levels of vehicle emissions. Autonomous vehicles’ role in urban areas raises its own set of challenges and opportunities, and if these vehicles are to help achieve transportation and climate objectives, cities will need to lay the groundwork for their arrival.

Much of the discussion in this chapter is necessarily speculative, given the enormous uncertainties in the evolution of the autonomous vehicles market and how these vehicles will be used. Autonomous vehicles’ impacts on transportation systems and the corresponding energy use and carbon emissions implications are far less certain and potentially much larger than vehicle-level fuel efficiency impacts of automation and connectivity technologies.

9.2 VEHICLE MILES TRAVELED

The availability of personal autonomous vehicles could result in increased vehicle miles traveled (VMT) in several ways: by making time spent in a car more productive or relaxing by allowing non-driving activities, by enabling people who cannot drive to travel in vehicles unaccompanied, by shifting trips from non-automobile modes to private automobile, and by allowing cars to drive without occupants. Taiebat et al. (2019) estimated an increase of 2% to 47% in average household VMT through rebound and induced demand associated with a complete shift to personal CAVs.

Autonomous vehicles could allow reduced VMT in certain activities, as in the case of searching for parking. Parking in urban areas is often a challenge to find, in addition to being costly if available. In many centers of activity, people typically circle the roads near their destination for some time in hope of finding a parking spot. Cookson and Pishue (2017) found that the average American driver spends 17 hours looking for parking every year, resulting in 1.7 billion gallons of additional fuel spent per year. A personal autonomous vehicle can drop its owners off at their destination, drive itself to any place where reasonably priced parking is available, and pick them up on demand. This practice would eliminate the miles driven and congestion caused in looking for a convenient parking spot, although the net impact on miles traveled would depend on the location of the parking identified by the autonomous vehicle and the potential change in the traveler’s destination based on a perceived level of difficulty in the trip.

On the whole, however, personal autonomous vehicles are expected to increase VMT. A scenario in which autonomous vehicles result in reduced VMT is one in which autonomous vehicles are largely fleet vehicles that carry more than one passenger. Modeling of the potential to reduce VMT through shared rides includes Magill (2018), which found an opportunity for 30% reductions in VMT, emissions, and transportation costs through a transition to ridesharing from the use of single-occupant vehicles. However, some industry analysts assert that electric autonomous vehicles could dramatically reduce the cost of ride hailing trips relative to today’s levels (UBS, 2017), which would lessen the incentive for ride hailing customers to share rides. Shared ride services are now jeopardized by the COVID-19 pandemic as well. Uber and Lyft suspended this user option in March 2020.

Suggested Citation:"9 Autonomous Vehicles." National Academies of Sciences, Engineering, and Medicine. 2021. Assessment of Technologies for Improving Light-Duty Vehicle Fuel Economy—2025-2035. Washington, DC: The National Academies Press. doi: 10.17226/26092.
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FIGURE 9.1 Range of estimated effects on energy use owing to changes in vehicle usage when ride hailing is implemented.
SOURCE: Reprinted from Transportation Research Part D: Transport and Environment, 70, Wenzel et al., Travel and Energy Implications of Ridesourcing Service in Austin, Texas, 18–34, Copyright (2019), with permission from Elsevier.

Several other effects may tend to increase autonomous ride hailing vehicles’ VMT. Based on experience to date with ride hailing companies using drivers, VMT may tend to increase when these services enter a new market owing to diversion of trips from transit and other modes as well as owing to miles driven without passengers at the beginning and end of the day and between fares. For example, Wenzel et al. (2019) estimate using data from RideAustin in Austin, Texas, that the net effect of ride hailing on energy use is a 41%–90% increase (Figure 9.1).

It should be noted that the Austin study’s conclusions that ride hailing services are likely to produce an increase in energy use relies upon (1) data from a service (RideAustin) in its early years of operation, and (2) assumptions regarding the level of modal shift and ride-sharing that draw from a nascent literature. The ride-sharing assumptions are crucial, in that a high level of ride-sharing is one of the key mechanisms that has been identified to allow autonomous vehicles to contribute to energy use reductions.

Anair et al. (2020) estimated that ride hailing results in 69% higher emissions than the rides it replaces. As in the RideAustin study, this result is driven by the prevalence of deadheading and the displacement of more energy-efficient transportation modes. On the other hand, Anair et al. find that in an alternative scenario of 50% pooled rides and electric ride hailing vehicles, ride hailing trips would reduce carbon emissions of the trips they replace by more than 50%.

9.3 VEHICLE OWNERSHIP MODELS

In ride hailing, delivery, and transit fleets, autonomous vehicles’ ability to operate without a driver could substantially reduce the cost of the transportation services they provide. Especially in high-density areas, the convenience and low cost of such services could not only expand their use but also induce many people to give up personal vehicles and use shared or other energy-efficient modes in place of driving.

Even automakers, whose growth has relied for decades on increasing levels of personal vehicle ownership, have reason to promote and respond to these fleet applications. Autonomous vehicles are likely to be expensive

Suggested Citation:"9 Autonomous Vehicles." National Academies of Sciences, Engineering, and Medicine. 2021. Assessment of Technologies for Improving Light-Duty Vehicle Fuel Economy—2025-2035. Washington, DC: The National Academies Press. doi: 10.17226/26092.
×

for some years after their introduction owing to their sensors, computers, and embodied intellectual property. They will also need frequent software updates and maintenance, at least at the outset. They also may be subject to limitations in where they can operate and raise privacy and security issues. They will certainly change the “driving” experience. Hence, despite the presumed safety and convenience of these vehicles, personal ownership may be limited for quite some time.

Fleets typically use vehicles much more intensively than do most owners. While an average light-duty vehicle in the United States is driven about 12,000 miles per year, an autonomous ride hailing vehicle might drive 60,000 miles per year or more (Barber et al., 2019; EIA, 2017). Consequently, fuel efficiency improvements to a fleet vehicle would achieve after a 3-year lifetime a present value of fuel savings almost 50% higher than the same improvements applied to a personal vehicle would achieve over its 15-year life (Barber et al., 2019). Hence fleets have greater incentive to invest in efficiency technologies, including electrification, for their vehicles.

These shared fleet vehicles will be refreshed more frequently, in either the top hat (upper vehicle body components that can be placed atop a common platform) or the powertrain, or both. With higher cost vehicles requiring regular updates of certain systems and components, fleets may choose to hold on to vehicles longer while swapping out powertrain and electronic components. Vehicles may become more like commercial aircrafts, in which “interiors/infotainment” are updated while the “shell” is reused. This refresh rate may lead to vehicles that begin to utilize more recycled material, reducing their overall carbon footprint.

9.4 VEHICLE CHARACTERISTICS

9.4.1 Vehicle Size and Weight

Autonomous vehicles present an opportunity to offer a new mobility model that can potentially be much cheaper (cost per mile traveled) than other means of transportation, particularly for trips with limited size and performance requirements, such as commuting. Such a vehicle could, for example, be configured for car sharing, autonomously transporting a single passenger with a small propulsion system. It might have a lower insurance premium (being safer than a human driven vehicle) and could offer a more economical alternative to using a full size, multiple-passenger car for commuting to work. This could result in significant fuel savings at this individual vehicle level, although the system-level impacts of such a mobility model would depend upon its effects on transit use, congestion levels, land use patterns, and how households met their needs for non-commute trips.

While such a mobility model may be speculative in U.S. passenger transport, there are numerous products being developed now in Asia and Europe with these features. Such vehicles are termed “quadricycles” in the European Union and, similar to U.S. low-speed vehicles, have limited vehicle weight, propulsion system power, and maximum speed, in general not exceeding 30 miles per hour (Regulation (EU) No 168, 2013). Task-specific choice of vehicle size and powertrain will most likely occur first in delivery fleets, which will deploy vehicles so as to minimize cost by optimizing size and performance for the given load.

Ride hailing fleets will also have an incentive to use vehicles with size and performance characteristics matching demand. An analysis of the fuel economy implications of this “rightsizing” effect found that these fleet vehicles would be smaller on average than today’s vehicles and, if compliant with the current size-based fuel economy standards, would have 20% higher average fuel economy as a result (Barber et al., 2019). Furthermore, as the passenger experience becomes more important than the driving or ownership values of consumers, other vehicle features will shift as well.

9.4.2 Fuel Efficiency

As discussed in Chapter 8, fuel efficiency improvements for individual vehicles from CAV technologies are largely realized at low levels of automation in tandem with connectivity. The vehicle fleet could achieve greater fuel efficiency if all vehicles were equipped and controlled so as to optimize the operation of the entire network rather than individual vehicle operation. In this case, travel times for individual vehicles might increase even while the efficiency of the system grows. Further energy use and cost reductions could follow from much reduced need

Suggested Citation:"9 Autonomous Vehicles." National Academies of Sciences, Engineering, and Medicine. 2021. Assessment of Technologies for Improving Light-Duty Vehicle Fuel Economy—2025-2035. Washington, DC: The National Academies Press. doi: 10.17226/26092.
×

for complexity and power in vehicles used in this way. While such systems may be technologically achievable with CAVs at low levels of automation, it is not clear that drivers would be prepared to accept reduced capabilities in their personal vehicles or externally imposed travel time increases before their vehicle is fully autonomous or substituted by a fleet vehicle. Moreover, such fundamental changes in people’s relationship with vehicles and driving might be tolerated only in congested areas.

While such a scenario is highly speculative at this point, it does illustrate how the adoption of autonomous vehicles could result in much higher average fuel efficiency. Modeling exercises are providing insight into the magnitude of the resulting energy savings.

9.5 RELATIONSHIPS AMONG AUTONOMY, CONNECTIVITY, SHARING, AND ELECTRIFICATION OF VEHICLES

Rapid electrification of vehicles along with the required charging infrastructure and a zero-emissions source of electricity are increasingly acknowledged to be essential to timely decarbonization of the transport sector. In a world of shared and autonomous vehicles, self-charging induction systems or self-docking systems will be needed. These vehicles will also need to be fast charged since downtime will be costly to their owners.

Autonomous vehicles do not require electrification, especially in rural areas. In urban areas, however, electric autonomous vehicles are likely to be the best option for ridesharing services. Urban planning and building codes will need to be modified to ensure charging infrastructure is available throughout the city. Furthermore, roadways need to be redesigned properly for multimodality. Autonomous electric vehicles can be also used as mobile energy storage and can be deployed to support and strengthen the grid in a utility-managed scenario, or enable microgridding through vehicle-to-building connectivity in emergency scenarios.

Electrification of CAVs may be motivated by certain synergistic effects present when a vehicle is connected, autonomous, and electric. Mass reduction enabled by CAV safety improvements presents an opportunity to reduce energy storage requirements without sacrificing range. Fully autonomous electric vehicles gain additional benefits such as the ability to refuel without a driver present where contactless charging is available and, in fleet applications, the ability to optimally assign vehicles according to the length of requested trips. Wireless charging is expected to improve substantially in the important attributes of power transfer, charging efficiency, and position accuracy. A notable example of the improvements is the 120 kilowatt wireless charging with 97% efficiency achieved in late 2018 (ORNL, 2018). Safety issues and other practical considerations will need attention. Robotic assistance and self-docking will be implemented to assist autonomous vehicle fleets, leveraging learnings from such implementations for hydrogen refueling stations.

9.6 COMBINED ENERGY IMPACTS OF AUTONOMOUS VEHICLES

9.6.1 Factor Analysis

A substantial body of literature on possible energy use impacts of autonomous vehicles has accumulated since 2014. A recent analysis by Argonne National Laboratory, Oak Ridge National Laboratory, and National Renewable Energy Laboratory synthesized that literature to identify a range of plausible energy impacts of autonomous vehicles owing to an array of factors (DOE, 2020). Results from that meta-analysis are the basis for the discussion in this section. It should be noted that this study assumes 100% penetration of autonomous vehicles in the fleet and hence does not represent realistic scenarios for 2025–2035. Projecting the effects of automation on the various factors at lower levels of penetration is difficult because these effects can be highly nonlinear in the technology penetration (Rios-Torres, 2020). The findings of the analysis are valuable nonetheless, because they demonstrate how the various factors are likely to interact in the long run. These insights can be used to help guide deployment of autonomous vehicles in the meantime so as to achieve energy savings ultimately, along with other beneficial outcomes.

The national laboratories’ approach was to use the results of many earlier studies to create probability distributions for the impacts of 24 individual factors on energy use. After adjusting for interactions between factors, they

Suggested Citation:"9 Autonomous Vehicles." National Academies of Sciences, Engineering, and Medicine. 2021. Assessment of Technologies for Improving Light-Duty Vehicle Fuel Economy—2025-2035. Washington, DC: The National Academies Press. doi: 10.17226/26092.
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FIGURE 9.2 Energy changes from each factor.
SOURCE: Gohlke (2020).

carried out a Monte Carlo analysis to evaluate the combined impact of all factors. Ninety percent of scenarios generated in this analysis produced a change in total energy use between 40% reduction and 70% increase, with an average increase in energy use of 10% (DOE, 2020). While substantially smaller than the range of possibilities found in an earlier national laboratory analysis (Stephens et al., 2016), this range of outcomes underscores the continuing uncertainty about the size and direction of the likely energy impacts of CAV adoption.

Of the 24 factors considered in the analysis, half were related to CAVs’ effects on travel demand and the other half to their effects on vehicle efficiency. Both travel demand and energy efficiency impacts included factors that increase energy use and factors that reduce it. Average effects of each of these factors are shown in Figure 9.2.

The national laboratory study also explored the sensitivity of the results with respect to assumptions about autonomous vehicles’ properties and/or travel behaviors, including the following: whether autonomous vehicles are battery electric vehicles, whether vehicles continue to be privately owned or are replaced by fleet vehicles, and whether rides are shared. Of particular relevance to the discussion in Chapter 8, the authors also investigated the effect of limiting vehicles to Level 2 automation and, separately, the effects of eliminating connectivity. Findings from these scenarios include the following:

  • Autonomous vehicles will increase energy use far more (24%) if the underlying fleet is electrified than if it is not (3%). This is primarily because energy efficiency benefits of CAV technologies for electric vehicles are lower than for internal combustion engine vehicles, as discussed in Chapter 8. Hence, VMT increase is the dominant effect in this case. Absolute energy use would still be relatively low in the electrified autonomous scenario given the efficiency of electric vehicles relative to internal combustion engine vehicles.
Suggested Citation:"9 Autonomous Vehicles." National Academies of Sciences, Engineering, and Medicine. 2021. Assessment of Technologies for Improving Light-Duty Vehicle Fuel Economy—2025-2035. Washington, DC: The National Academies Press. doi: 10.17226/26092.
×
  • If autonomous vehicles are fleet vehicles, the total VMT increase is smaller than if they are privately owned; and the fleet vehicles benefit more than private vehicles from the direct energy efficiency improvements of the CAV technologies. The net result is that total energy increases by 5% in an all-fleet scenario, compared with a 29% energy use increase in an all-private-autonomous vehicle scenario.
  • If CAVs were limited to Level 2 automation, total energy use would decline by 34% relative to a non-CAV status quo. That is because these vehicles would achieve the full fuel efficiency benefit of connectivity and automation while VMT would actually be 9% lower than in the non-CAV scenario.
  • Alternatively, with fully autonomous vehicles but no connectivity, VMT would increase somewhat less than in the baseline but vehicle efficiency would improve by only 6% (versus 21% in the baseline). Energy use would increase by 25%.

9.6.2 Technology Adoption

The energy impacts of autonomous vehicles will depend on their pace of adoption as well as the many factors described in Section 9.6.1. This applies not only to the increases or decreases that follow directly from the autonomous capabilities themselves but to an even greater extent to the system effects of autonomous vehicles. Speed harmonization and other congestion-reducing effects, for example, will follow only when a substantial fraction of the fleet is autonomous.

9.6.2.1 Determinants of Adoption

Autonomous vehicles offer potential buyers an array of benefits that could help drive their adoption. The continued innovation and fast pace of technology advances in autonomous vehicle development encourages automakers and suppliers to continue investing in the technology and to investigate revenue generation opportunities as offshoots of the technology, such as new modes of urban tourism (Cohen and Hopkins, 2019).

However, the enthusiasm on the part of consumers and automakers is not without reservation, as there are still several issues to resolve that, along with commercialization at a reasonable price point, are impeding a quick introduction of the technology. These include the following:

  • Technical issues affecting safety: For example, achieve more robust object identification and precise positioning.
  • Cybersecurity issues: Ensure complete hardening of defense against any possible malicious attack.
  • Regulatory issues: Approve policies and regulations governing the operation of autonomous vehicles and sharing roads with conventional vehicles and other users.
  • Infrastructure readiness: Achieve adequate coverage by digital maps and/or roadway connectivity devices, including in rural areas, and dedicate lanes as needed for autonomous operation.
  • Privacy issues: Establish protocols for automakers’, governments’, and third parties’ access to and use of the highly detailed data generated by connected vehicles.
  • Legal and liability issues: Establish a consistent legal framework for assignment of liability in case of crash or malfunction that is acceptable to industry and consumer interests.
  • Customer acceptance: Expand fraction of public that trusts the operation and security of the technology and values its benefits over the “driving pleasure” of non-autonomous vehicles.

In the area of consumer acceptance alone, researchers have identified multiple factors relevant to autonomous vehicle adoption rates, including safety, performance-to-price value, mobility benefits, value of travel time, symbolic value, and environmental friendliness (Jing et al., 2020). Behavioral approaches introduce additional factors such as perceived ease of use, perceived usefulness, and social norms. Determinants of autonomous vehicles’ adoption for personal use, ride hailing service, or transit services include attitudes toward the environment, collaborative consumption, and car ownership (Acheampong and Cugurullo, 2019).

Litman (2020) cites consumer travel and housing preferences as well as development practices and other public policies as further determinants of autonomous vehicle adoption rates. Thus, the range of relevant factors

Suggested Citation:"9 Autonomous Vehicles." National Academies of Sciences, Engineering, and Medicine. 2021. Assessment of Technologies for Improving Light-Duty Vehicle Fuel Economy—2025-2035. Washington, DC: The National Academies Press. doi: 10.17226/26092.
×

is wide, and many are difficult to predict given the dramatic departure autonomous vehicles represent from current driving and travel options. However, understanding the roles and relationships of these many factors is important to anticipating and guiding the trajectory of autonomous vehicles so as to realize their benefits and avoid adverse consequences.

9.6.2.2 Market Penetration

Several automakers have postponed their projected release dates for fully autonomous vehicles in recent years, and the COVID-19 pandemic is causing further delays and attrition among the suppliers and engineers working on these technologies (Bloomberg, 2020). The industry’s timeline has lengthened accordingly. Substantial sales are still anticipated over the next decade, however, with fleet sales starting to ramp up by 2025 and personal vehicles following around 2030.

Figure 9.3 shows several scenarios of automated vehicle market penetration from McKinsey (Gao et al., 2016), including a “low-disruption” scenario in which fully autonomous vehicles reach only a few% of the market by 2035 and a “high disruption” scenario in which they reach two-thirds market penetration by 2035. More recent commercial projections from IHS Markit, Deloitte, and others continue to include a wide range of sales trajectories (IHS Markit, 2018; Schiller et al., 2020; Murray, 2014; Alexander and Gartner, 2014; Lanctot, 2017; Gibson, 2018; Forsgren et al., 2018), with some even anticipating an autonomous-only vehicle market by the early 2030s (Mayor et al., 2018). These projections can be difficult to interpret absent stated assumptions regarding the full range of adoption factors, including changes in vehicle ownership patterns, use of shared ride services, and practices in home delivery.

Owing to the multiple dimensions of uncertainty, much of the academic research on autonomous vehicle adoption stops short of projecting the trajectories of sales or fleet penetration (Talebian and Mishra, 2018; Shabanpour et al., 2018). Some such projections do exist, however. For example, Bansal and Kockelman (2016) simulated CAV adoption scenarios defined by consumers’ willingness to pay, technology price reductions of 5% or 10% per year,

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FIGURE 9.3 Market share of fully autonomous vehicles. SOURCE: Exhibit from Gao et al., “Disruptive trends that will transform the auto industry,” January 2016, McKinsey & Company, www.mckinsey.com. Copyright (c) 2021 McKinsey & Company. All rights reserved. Reprinted by permission.
Suggested Citation:"9 Autonomous Vehicles." National Academies of Sciences, Engineering, and Medicine. 2021. Assessment of Technologies for Improving Light-Duty Vehicle Fuel Economy—2025-2035. Washington, DC: The National Academies Press. doi: 10.17226/26092.
×

and technology adoption regulations. The simulation was calibrated with results from a consumer survey. Across eight scenarios, they found that sales share of Level 4 automation would reach 10%–34% by 2030, 15%–44% by 2035, and 19%–75% by 2040. A subsequent analysis drawing on the theory of diffusion of innovations and using results from a survey of university employees found lower sales shares of 1%–5% in 2030, 5%–25% in 2035, and 8%–60% in 2040, based on annual reductions in price of 5%–20% (Talebian and Mishra, 2018). It is worth noting that both cited academic analyses gave very wide ranges in the projected sales of autonomous vehicles in 2030, 2035, and 2040.

9.7 AUTONOMOUS VEHICLES AND ENERGY USE: POLICY ISSUES

After several years of study by researchers and assessments by practitioners in various fields, the range of plausible energy impacts of the adoption of autonomous vehicles includes large positive and large negative values. While some part of this uncertainty can be attributed to the fact that autonomous vehicles are not in general use today and hence their impacts are speculative, the large, indeterminate energy and emissions impact of their deployment is also indicative of the need for public policies to promote favorable outcomes. Factors affecting VMT and vehicle efficiency will contribute significantly to the net energy impacts, so policies regarding both usage and efficiency merit consideration. This section begins with a discussion of issues relating specifically to fuel economy regulation and concludes with an overview of other areas where policies might be considered.

9.7.1 Autonomous Vehicles and Fuel Economy Standards

Commercialization of autonomous vehicles will raise a variety of issues relevant to fuel economy standards. These relate not only to the fuel economy of the vehicles themselves but also to possible changes in vehicle ownership models and usage.

If autonomous vehicles experience very low crash incidence, there could be an opportunity to dramatically lightweight vehicles upon full transition to an autonomous fleet. That will not occur within the time horizon of this study (2035), however. A study from the Insurance Institute for Highway Safety finds also that two-thirds of crashes could still occur in an all-autonomous environment unless autonomous vehicles are programmed to give priority given to safety protocols over occupant preferences when the two conflict (Mueller et al., 2020). Automakers consider safety heavily in their autonomous vehicle programs, however, so they are highly likely to program their vehicles accordingly.

9.7.1.1 Ownership Models

As noted above, to the extent that autonomous vehicles contribute to a shift away from personal ownership of vehicles and toward fleet ownership, they could alter the profile of the future fleet, moving it toward smaller, less powerful vehicles on average, with vehicles having special capabilities or high carrying capacity largely dedicated to applications requiring those capabilities. The current structure of fuel economy standards can accommodate shifts in the sales distribution of vehicle classes, in that the standard for each automaker self-adjusts to the size and type of vehicles sold each model year.

There is no similar accommodation for a shift in performance needs, however, so the agencies will need to factor any such shift into their calculation of achievable levels of fuel economy. Recent fuel economy and greenhouse gas emissions rulemaking analyses have segmented the market into “performance” and “non-performance” vehicles for purposes of assessing technology effectiveness and penetration. A similar approach could be applied to account for increasing fleet ownership of vehicles, assuming the agencies can make reasonable projections of such trends. Alternatively, fleet vehicles might be regulated under separate standards, given that both vehicle characteristics and usage patterns will differ substantially from those of personal vehicles. The high annual mileage and resultant accelerated payback of incremental costs associated with fleet usage should generally improve the cost-effectiveness of fuel economy technologies, raising achievable fuel economy levels.

Suggested Citation:"9 Autonomous Vehicles." National Academies of Sciences, Engineering, and Medicine. 2021. Assessment of Technologies for Improving Light-Duty Vehicle Fuel Economy—2025-2035. Washington, DC: The National Academies Press. doi: 10.17226/26092.
×

A shift from personal to fleet ownership would also mean a smaller vehicle stock, since each vehicle would meet the needs of multiple users. This would not necessarily mean reduced vehicle sales, since fleet vehicles would be replaced more frequently. However, if these fleets achieved high average occupancy, the vehicle stock would presumably be further reduced and sales would be lower. These factors will warrant consideration in future standards-setting if they substantially affect the dynamics of vehicle sales.

If high annual miles and other characteristics of fleet service were to alter the relative lifetimes of vehicle body, powertrain, and electronic systems and lead to large-scale reuse of major vehicle parts and systems affecting efficiency, implications for fuel economy regulation could be substantial. The definition of “new vehicle” and of regulated parties would need to be reconsidered to prevent deterioration of the standards’ relevance for real-world fuel economy.

9.7.1.2 Usage Patterns

Autonomous vehicles’ possible effects on VMT raises several questions of potential relevance to fuel economy standards. In personal use, autonomous vehicles could induce additional travel by reducing the cost of driving, especially in the form of time freed up for other activities. This phenomenon is similar to the rebound effect associated with improved fuel economy. However, Taiebat et al. (2019), using a microeconomic model to estimate elasticities of VMT demand with respect to fuel and time costs, found that households had much greater sensitivity to time costs than to fuel costs. If autonomous vehicles in fact are found to have substantially higher VMT than the vehicles they replace, this should be reflected in the analysis of achievable fuel economy, since present value of fuel savings from an increase in efficiency will be higher for a vehicle that accumulates miles more quickly. Furthermore, if fuel economy standards were found to affect autonomous vehicles’ sales share, rebound associated with autonomous vehicle time savings should be considered in the analysis of the standards’ effects.

In fleet use, autonomous vehicles’ effects on VMT are indeterminate, but some have advocated that the high potential for shared rides and or high mileage accumulation in ride hailing fleets should be rewarded in fuel efficiency standards. In the Safer Affordable Fuel Efficient (SAFE) Vehicles Notice of Proposed Rulemaking, the National Highway Traffic Safety Administration and the U.S. Environmental Protection Agency requested comment on the idea that autonomous vehicles “placed in ridesharing or other high mileage applications” might be eligible for credits because the “per-mile emission reduction benefits would accrue across a larger number of miles for shared-use vehicles” (NHTSA/EPA, 2018). It is not clear that lifetime mileage for these vehicles would be higher than for personal vehicles, however; they might instead move to the resale market in a few years and be scrapped at an earlier age than privately owned cars are, as is the case with rental cars today. With regard to credits for shared-ride vehicles, predicting the rate of sharing could be quite difficult and the extent to which these vehicles divert riders from transit and non-motorized modes remains to be seen. An additional consideration related to fleet ownership of autonomous vehicles is that these vehicles may be sold as personal vehicles in the secondary market. Hence, much more data on autonomous vehicle usage patterns would be needed to support any assumptions regarding their VMT-based effects on energy use.

9.7.2 Other Energy-Related Policy Options for Autonomous Vehicles

Policies already being pursued or considered at various levels of government to slow or reverse VMT growth will be relevant to autonomous vehicles. These policies include modernization and expansion of transit services and land use planning to ensure accessibility to most destinations by non-auto modes and minimize the need to drive. They also include mileage-based user fees, which could be easily implemented for autonomous vehicles to address a variety of special considerations and circumstances using their data and communications capabilities. Mileage fees could be used, for instance, to promote efficient use of autonomous vehicles by increasing rates for zero-occupant vehicles or reducing them for high-occupant vehicles.

Such policies are already in use for ride hailing vehicles. For example, as of January 2020, the city of Chicago collects surcharges on ride hailing trips in the central business district of $3.00 for solo rides and $1.75 for shared

Suggested Citation:"9 Autonomous Vehicles." National Academies of Sciences, Engineering, and Medicine. 2021. Assessment of Technologies for Improving Light-Duty Vehicle Fuel Economy—2025-2035. Washington, DC: The National Academies Press. doi: 10.17226/26092.
×

rides (Uber, 2019). The charges are intended to address the congestion caused by ride hailing vehicles and generate revenue for mass transit upgrades (Spielman, 2019). Such considerations will become more pressing with the advent of autonomous vehicles in these fleets. A group of international nongovernmental organizations working to promote livable cities developed the Shared Mobility Principles for Livable Cities (2017), among them the principle that autonomous vehicles must be shared in urban areas. Cities could also help to ensure that autonomous ride hailing supports transit services by reducing charges for trips accessing transit.

The state of California has adopted targets to reduce greenhouse gas emissions per-passenger-mile for ride hailing companies to push these companies to prioritize shared rather than single-passenger ride hailing trips and to promote the use of low emissions vehicles in their fleets. Additional goals of California’s program include supporting usage of transit and micro-mobility, and maximizing equity of access to transportation services (CARB, 2019).

Other strategies to ensure that autonomous vehicle adoption reduces energy consumption include policies to discourage ownership of autonomous vehicles for personal use; giving priority access to curb space, parking facilities, and designated highway lanes to multiple-occupant vehicles; reducing travelers’ reluctance to share rides by providing advanced information about fellow riders and installing personal safety measures; creating integrated systems of “Mobility as a Service” as the local level; maximizing the convenience of travel without personal vehicles; and prioritizing the deployment of autonomous vehicles for transit and micro-transit services (Greenwald and Kornhauser, 2019).

9.8 FINDINGS AND RECOMMENDATIONS

FINDING 9.1: The energy implications of autonomous vehicles will be determined to a large degree by their effects on peoples’ mode choices, vehicle miles traveled, and other travel behaviors. Research to date indicates that at full penetration autonomous vehicles could plausibly produce impacts ranging from a 40% reduction to a 70% increase in energy consumption. Absent new policies, autonomous vehicles will tend to reduce the cost of driving and therefore increase miles driven, perhaps very substantially. To the extent that they are used for shared rides and/or they are more likely than other vehicles to be electric, they will reduce transportation energy use.

FINDING 9.2: A second major determinant of the energy impacts of autonomous vehicles will be expectations of vehicle performance and features. Purchasers of autonomous vehicles are likely to prioritize comfort, convenience, and affordability rather than engine horsepower or acceleration. Fleet-owned autonomous vehicles will be right-sized, based on their intended purpose. Autonomous vehicles that are operated cooperatively with the surrounding traffic in urban or congested areas can achieve very high fuel economy, although perhaps with a cost in travel time for some individuals.

FINDING 9.3: Autonomous driving capability is likely to add at least $5,000–$7,500 to the cost of any vehicle sold with such capability in the next decade. Ensuring safety under all conditions, resolving cybersecurity issues, developing appropriate regulations, and gaining consumer acceptance of a radically different driving experience is likely to take even longer. Consequently, fleets and other users with special needs are likely to drive the market for autonomous vehicles through 2030; earlier industry projections of substantial sales before 2025 were overly optimistic. Autonomous vehicles’ share of the market in 2035 is highly uncertain but likely to fall in the 0%–40% range, with ride hailing and delivery fleets accounting for 40%–60% of those sales.

FINDING 9.4: Fleet autonomous vehicles will be purpose-built and will reflect the needs of ride hailing and delivery companies. They will differ from typical vehicles for personal use in terms of size, body type, power, and luxury. They may be more likely to be electric as well, given high power needs, high urban usage, and ability to guide themselves to a charging station. Usage patterns (annual mileage, scrappage rates, etc.) also will differ from those for personal vehicles. In dense urban areas, micro-mobility products not currently subject to Corporate Average Fuel Economy standards may replace many personal automobiles.

Suggested Citation:"9 Autonomous Vehicles." National Academies of Sciences, Engineering, and Medicine. 2021. Assessment of Technologies for Improving Light-Duty Vehicle Fuel Economy—2025-2035. Washington, DC: The National Academies Press. doi: 10.17226/26092.
×

RECOMMENDATION 9.1: Prior to the advent of autonomous vehicles, the National Highway Traffic Safety Administration (NHTSA) should consider in detail the ways in which autonomous vehicle properties and usage will differ from non-autonomous vehicles and how these differences should be reflected in the stringency and structure of fuel economy standards. NHTSA should consider regulating fuel efficiency of autonomous vehicles for fleet use differently from personally owned vehicles. Maximum feasible standards for these vehicles could be substantially more stringent than standards for personally owned vehicles; a requirement that autonomous vehicles be zero-emission vehicles should be considered, especially in urban areas.

RECOMMENDATION 9.2: To achieve the fuel-savings potential of autonomous driving and avoid its unintended consequences, the U.S. Department of Transportation (DOT) should consider actions to guide the effects of autonomous driving on the U.S. transportation system. This includes pricing strategies that promote sharing of autonomous vehicles and their complementarity to less energy-intensive modes. DOT should begin now to develop and provide information to other agencies and to Congress to highlight the need for policies to guide autonomous vehicle deployment.

RECOMMENDATION 9.3: While developing requirements and protocols to address cybersecurity and privacy concerns associated with autonomous vehicles, the U.S. Department of Transportation should also ensure that data generated by these vehicles is used to understand driving behavior, usage patterns including occupancy and relationship to other modes, and real-world fuel efficiency.

RECOMMENDATION 9.4: Given potential implications of autonomous vehicle adoption for energy use, emissions, and land use development patterns, the U.S. Department of Transportation should work with the U.S. Department of Energy, U.S. Environmental Protection Agency, and U.S. Department of Housing and Urban Development to support research and policies that advance the simultaneous achievement of the safety, economic, environmental, and equity benefits that autonomous vehicles can provide.

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Suggested Citation:"9 Autonomous Vehicles." National Academies of Sciences, Engineering, and Medicine. 2021. Assessment of Technologies for Improving Light-Duty Vehicle Fuel Economy—2025-2035. Washington, DC: The National Academies Press. doi: 10.17226/26092.
×

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Suggested Citation:"9 Autonomous Vehicles." National Academies of Sciences, Engineering, and Medicine. 2021. Assessment of Technologies for Improving Light-Duty Vehicle Fuel Economy—2025-2035. Washington, DC: The National Academies Press. doi: 10.17226/26092.
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From daily commutes to cross-country road trips, millions of light-duty vehicles are on the road every day. The transportation sector is one of the United States’ largest sources of greenhouse gas emissions, and fuel is an important cost for drivers. The period from 2025-2035 could bring the most fundamental transformation in the 100-plus year history of the automobile. Battery electric vehicle costs are likely to fall and reach parity with internal combustion engine vehicles. New generations of fuel cell vehicles will be produced. Connected and automated vehicle technologies will become more common, including likely deployment of some fully automated vehicles. These new categories of vehicles will for the first time assume a major portion of new vehicle sales, while internal combustion engine vehicles with improved powertrain, design, and aerodynamics will continue to be an important part of new vehicle sales and fuel economy improvement.

This study is a technical evaluation of the potential for internal combustion engine, hybrid, battery electric, fuel cell, nonpowertrain, and connected and automated vehicle technologies to contribute to efficiency in 2025-2035. In addition to making findings and recommendations related to technology cost and capabilities, Assessment of Technologies for Improving Light-Duty Vehicle Fuel Economy - 2025-2035 considers the impacts of changes in consumer behavior and regulatory regimes.

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