In the United States over the next ten years, governments may spend some $1.5 trillion on their roadways, consumers may purchase vehicles worth nearly $3 trillion, property owners may develop millions of acres of rural land, and the US Postal Service may drive its cars and trucks approximately 12 billion miles (with FedEx alone adding 10 billion miles more). How might these massive numbers—and others like them—be harnessed to smooth the deployment of self-driving vehicle technologies?

While dramatic changes to infrastructure, vehicles, or legal regimes may be warranted, this blog post instead considers some low-hanging fruit: modest steps that public and private actors might take now to reduce the costs of adopting and adapting to these emerging technologies down the road.

These actors would do well to consider the potential for increased vehicle automation when they make decisions about infrastructure and procurement projects. Numerous formal mechanisms in the public sector already provide analogies (or, in some cases, opportunities) for such an exercise, including administrative delegation, cost-benefit analysis, value engineering, environmental assessment, smart growth criteria, aesthetic appropriation, socioeconomic preference, and design guidance. Key questions might include:

How does the project timeline correspond to plausible timelines for vehicle automation?

Could self-driving vehicles change assumptions that underlie the project?

Could the project be used to create demand for self-driving vehicles?

Could the project mitigate technical or economic barriers to vehicle automation?

Could the project generate outputs that would be useful to self-driving vehicles?

Take physical infrastructure. The next ten years could see the acquisition of tens of billions of dollars in right of way and the construction of hundreds of miles of carpool, toll, bus, and possibly truck lanes; modifications to these projects might enable the separation of closely-spaced vehicle platoons from other traffic. Similarly, tens of millions of road signs and billions of dollars in traffic signal equipment may be purchased over the next decade; particular design specifications might help self-driving vehicles communicate with or recognize these control devices. Conversely, self-driving vehicles could impact demand and capacity assumptions for current transportation projects that may not be finished for decades.

Or take digital infrastructure. In the coming years, states will likely conduct or commission numerous roadway inventories and invest in construction, congestion, and incident management systems, and private companies will likewise collect vast amounts of roadway data. The accessibility and integrity of these data may be crucial to the performance of certain self-driving vehicle technologies. In addition, existing fleets—such as the 213,881 vehicles operated by the US Postal Service—might also be well-positioned to collect data for detailed mapping, machine learning, and scenario testing.

Finally, consider the vehicles themselves. A car or truck that is sold in 2022 might still be on the road in 2032—and will incorporate thousands of dollars in component electronics. The ease with which such a vehicle might be converted to driverless operation could depend on how its electronic systems, particularly its CAN bus or other internal communications network, have been designed. As regulator, the federal government already influences system design: It mandates certain systems, like electronic stability control and on-board diagnostic test equipment, and may soon mandate others, like event data recorders and external communication equipment. As customer, the public sector could also influence design by choosing what vehicles to buy and when to buy them. For example, the federal government (including the US Postal Service) owns or leases almost half a million vehicles, and New York City’s “taxi of tomorrow” concession is expected to generate at least $1 billion in sales for Nissan.

There are downsides to this kind of forecasting: Governments sometimes get ahead of themselves or otherwise make mistakes. (So does the private sector.) Ultimately, however, this complexity may demand that relevant actors perform more rather than less analysis of the potential risks and opportunities that self-driving vehicles present. Now might be a good time to begin.