High-level structure of the CAV Tipping Point Model, where the 

arrows represent influences and feedbacks between sectors or 

stakeholders. (Source: NREL)

Analytical Methods: The CAV 

tipping point model includes a 

broad range of stakeholders

The information contained in this poster is subject to a government license. Automated Vehicles Symposium

San Francisco, California

July 9-12, 2018 NREL/PO-5400-71816

Analysis of Tipping Points in Connected and 

Automated Vehicle (CAV) Adoption Scenarios

Brian Bush, Laura Vimmerstedt, and Jeff Gonder (NREL) 

This work was authored by Alliance for Sustainable Energy, LLC, the manager and operator of the National Renewable Energy Laboratory for the U.S. Department of Energy (DOE) under Contract 

No. DE-AC36-08GO28308. Funding provided by U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Vehicle Technologies Office. The views expressed in the article do 

not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. 

Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes.

Manufacturers

Travel Distance

Travel Time

Activities

Vehicles

Population

Insurers

Regulators Infrastructure

Choice

Vehicle Requirement Energy

Safety

Comparison of simulations with higher and lower L1 costs and 

behavioral preference for L4 vehicles: The figure shows regimes 

with (from left) little CAV adoption, L1 adoption, both L1 and L4 

adoption, and L4 adoption. Fuel consumption without CAVs 

reflects vehicle efficiency improvements. The figure illustrates 

capabilities to explore different end states. (Source: NREL)

Best fuel 

consumption 

outcomes.

Worst fuel 

consumption 

outcomes.

Sensitivity analysis of fuel consumption by concept type 

nationally in 2040 as a function of the operating cost for L1 and 

L4 CAV technologies and consumer preference for using CAVs. 

These results show a rapid separation of end states dominated 

by CAVs (left and top sides of figure) versus end states where 

CAVs play a minor role (right bottom corner of figure). This 

highlights the need for quantitative understandings of both 

consumer preferences and also operating costs for CAV 

technologies. (Source: NREL)

Sensitivity analysis of size of taxi fleet as a function of 

consumers’ preference for using CAVs and the value consumers 

place on their time: These results suggest the possibility that the 

overall taxi or ride-source vehicle fleets may increase in 

scenarios where consumers show a substantial preference for 

the use of automated taxis (right side of figure), provided that 

such taxis are not disadvantaging in terms of travel time relative 

to non-taxis. (Source: NREL)

Regression tree showing major influences on energy 

consumption. Each pie chart shows the fraction of results that lie 

in the best 5% of fuel consumption, within simulations selected 

by the preceding branching criterion. (Source: NREL)

Summary of the frequency of different bottlenecks to CAV 

adoption among the scenarios. (Source: NREL)

Fuel consumption outcomes for scenarios in the screening study. 

Each corner of the triangle represents a “pure” L0, L1, or L4 

vehicle fleet and points in between those corners represent 

outcomes with mixes of those vehicle types. (Source: NREL)

Distribution of energy outcomes for scenarios, distinguished by 

the predominance of L4 CAVs in the scenarios. (Source: NREL)

The results show different outcomes for CAV adoption and system-wide fuel use, given behavioral 

parameters such as consumer preferences (e.g., value of time) and financial parameters (e.g., operating 

cost).

Objectives: Explore transitions to 

large-scale CAV adoption

• Identify tipping points to large-scale adoption

• Understand sensitivity of adoption to key factors

• Examine technological, economic, demographic, and

regulatory issues

• Highlight relevant data and data needs

• Explore effects of policy through scenario analysis

Conclusions: The dynamic interplay 

of factors (e.g., stakeholder 

environment, costs, vehicle 

preferences, and freed time) 

contributes to large-scale CAV 

adoption scenarios.

• Interaction and time delays of “stage gates” can summarize

CAV scenario complexity.

• Synergies between technology pathways, CAV concepts, and

adoption behavior lead to multiple potential end states.

• Freed time from driving appears to be a compelling CAV

advantage.

• Long-term energy outcomes of various CAV scenarios differ by

half an order of magnitude, contingent on deadhead miles,

vehicle occupancy, and drivetrain variation.

This poster and the work described were sponsored by the U.S. Department of Energy (DOE) Vehicle Technologies Office (VTO) under the Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility 

Laboratory Consortium, an initiative of the Energy Efficient Mobility Systems (EEMS) Program. The authors acknowledge Eric Rask of Argonne National Laboratory for leading the Connected and Automated Vehicles Pillar of 

the SMART Mobility Laboratory Consortium. The authors would particularly like to thank David Anderson and Michael Berube with DOE’s Office of Energy Efficiency and Renewable Energy (EERE) for helping to establish the 

project concept and advance its implementation, and for providing ongoing guidance and support.

Future Work: Extension to 

commercial delivery, application at 

an urban regional scale, and data 

enhancements are expected to 

improve understanding of tipping 

points for transition to large-scale 

CAV adoption.