U.S. Department of Energy (USDOE) modeling of connected and automated vehicle (CAV) impacts using multiple deployment configuration scenarios.
Nationwide, United States
Estimated Bounds and Important Factors for Fuel Use and Consumer Costs of Connected and Automated Vehicles
Summary Information
This study conducted by the National Renewal Energy Laboratory estimated ranges of potential effects of connected and automated vehicle (CAV) technologies on vehicle miles traveled (VMT), vehicle fuel efficiency and cost to consumers.
Three CAV scenarios were considered to calculate upper and lower bounds of their potential impacts on consumer costs:
- Partial: Partial automation with some connectivity
- Full-No Rideshare: Full automation with high connectivity without ridesharing
- Full-With Rideshare: Full automation with high connectivity with ridesharing.
The scenario with partial automation was assumed to include technologies such as driver assistance that required an attentive driver to control the vehicle, with limited connectivity. Scenarios with full automation were assumed to allow vehicle operation without an attentive driver, with connectivity that permitted communication between travelers, vehicles, traffic control devices, and traffic control centers. Ridesharing referred to a net increase in vehicle occupancy resulting from two or more people riding together in a vehicle during some or all of their travel.
Data input into the model to evaluate potential CAV technology impacts on costs to consumers was derived from relevant information extracted from literature for separate cost categories. Costs in the following categories, spanning vehicle purchase and operation, were considered:
- CAV technology cost increment to vehicle purchase price
- Maintenance and repair costs
- Connectivity service fee
- Insurance premiums
- Costs of crashes not covered by insurance
- Fuel cost
- Cost of travel time.
Findings
- Compared to the conventional baseline, most CAV scenarios showed substantial decreases in costs to consumers.
- The lower end assumptions in the "Full-With Rideshare" scenario generated the largest estimated cost reduction (roughly 60 percent relative to the base scenario on a cost per passenger mile basis, when accounting for the cost of travel time).
- The upper-end assumptions for the Partial automation scenario produced the only cost increasing case relative to the baseline. For this case, assumptions of higher vehicle purchase price and repair costs together with little or no benefits with respect to insurance and travel time costs resulted in a net 3 to 4 percent cost increase relative to the baseline.