U.S. Department of Energy (USDOE) modeling of connected and automated vehicle (CAV) impacts using mutliple 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.
Constraints for travel demand and efficiency impact range estimates were derived from previous studies that evaluated various CAV technology effects on conventional vehicle operation. The identified CAV technology influences taken into account included:
- Drive profile and traffic flow smoothing
- Faster travel
- Intersection vehicle-to-infrastructure (V2I)/infrastructure-to-vehicle (I2V) communication
- Collision avoidance
- Platooning
- Vehicle/powertrain resizing.
The analysis assumed full CAV penetration and combined the ranges of CAV technology effects on vehicle miles traveled (VMT) and fuel consumption rates over the total U.S. light duty vehicle (LDV) stock. These calculations produced lower- and upper-bound estimates of potential total U.S. LDV fuel use (and greenhouse gas emission) impacts for three CAV scenarios relative to a present-day base scenario.
Major Assumptions Defining the Scenarios and Bounds for the Light-Duty CAV Energy Impact Assessment
| Scenario | Automation and Connectivity Level | Ridesharing | Upper or Lower Bound | Efficiency Improvement | VMT Increase |
|---|---|---|---|---|---|
| Conventional | None | No | N/A | N/A | N/A |
| Partial | Partial | No | Upper | Low | High |
| Lower | High | Low | |||
| Full-No Rideshare | Full | No | Upper | Low | High |
| Lower | High | Low | |||
| Full-With Rideshare | Full | Yes | Upper | Low | High |
| Lower | High | Low |
Scenarios with partial automation were 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 permitting 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.
Findings
The results showed wide separation between the scenarios’ upper and lower bounds on U.S. LDV fuel use, reflecting the large uncertainties in CAVs’ impacts on both vehicle fuel consumption rates and VMT.
The partial automation scenario showed a relatively modest range of impacts, on the order of ±10 percent for the upper and lower bounds relative to the base scenario. However, the full-automation scenarios showed wide separation between the bounds on U.S. LDV fuel use, reflecting the large uncertainties in fully automated CAV influences on both VMT and vehicle fuel efficiency.
The upper bound for the Full-No Rideshare scenario represents the highest increasing fuel use case with triple the annual fuel use of the base scenario. The lower bound of the Full-With Rideshare scenario represents the lowest decreasing fuel use case with a more than 60 percent reduction against the base scenario’s fuel use.
