USDOT Publishes Automated Driving System Demonstration Grant Applicants

USDOT Publishes Automated Driving System Demonstration Grant Applicants

March 28, 2019  | Alice Grossman

March 28, 2019

On March 26, USDOT published the list of applicants for the Automated Driving System (ADS) Demonstration Grants which were announced in December, 2018. The ADS Demonstration program will fund public agencies or institutions for planning, research, and demonstration of the integration of automated driving systems into the national transportation system. USDOT approved $60 million in Federal funding towards the grants, with an award ceiling of $10 million per single grant award.  Online FAQs also state an intention to award no more than $15 million to any individual state, though the Notice of Funding Opportunity (NOFO) has no mention of an award cap by state. This means that projects in individual states may end up competing against each other to a certain extent, which many agencies seem to be prepared for given the redundancy in submission from the same agency and the likelihood of agencies collaborating on multiple submissions (only lead agencies are listed on the USDOT website).

USDOT received 73 applications from cities, counties, metropolitan planning organizations, states, transit agencies, other transportation authorities, and public universities. Not surprisingly given their history of a test bed for automated vehicles technologies, California houses nine of the submitted projects, matched for the most submittals with the state of Florida. Highlighting a focus on research, 23 of the proposals come from universities, including four from the Virginia Tech Transportation Institute alone. About a fifth of the applications (16) come from cities, 13 from state DOTs (including two submissions from FDOT), and then a variety from transportation agencies such as transit agencies, and toll way authorities. The Oklahoma Indian Nations Council of Governments also submitted and stands out as the only project with a tribal nation lead.

Applicants are likely to have brought in state and local governments and transit agencies as contributing partners providing resources such as direct funds, personnel time, supplies and equipment, physical or digital infrastructure, and expertise. USDOT did not require matching funds for proposed projects, but did state that it is both encouraged and will be considered in the award selection process.

Though the actual submissions aren’t currently available to the public, requirements in the 44-page NOFO suggest that proposals will focus on safety outcomes of implemented ADS technology, especially as it can inform future analysis and rulemaking at the national level. More in-depth focus areas are listed as: significant public benefit(s); addressing market failure and other compelling public needs; economic vitality; complexity of technology; diversity of projects; transportation-challenged populations; and prototypes. Furthermore, projects must include a data element with the proposal including a data management plan as well as an evaluation plan that ties data to project objectives.

With a few commonalities sure to emerge, the variety of focus areas and types of agency leads is likely to lead to disparate project types. Proposals are likely to include vehicle types ranging from sedans and SUVs, to low-speed shuttles, to transit busses. Given the range in lead agencies, operational design domains will also likely run a gamut from city streets to rural or highway corridors, to university or other special use campuses.

Proposals that are well thought-out, data driven, and truly collaborative between operating, governing, and research agencies will give the USDOT the opportunity to select projects that will help advance national understanding of the potential safety benefits of ADS technologies. Well-designed collaborations can help move policy and practice forward by filling holes created by previous reluctance from technology companies and OEMs to share data at a level that can support robust safety analysis has led to a dearth in data-driven performance analysis of new technologies and transportation modes.

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