To procure datasets that document how connected and automated vehicles (CAVs) operate in traffic and how adjacent vehicles, both human driven and other CAVs, behave in the presence of the subject CAV.

The Federal Highway Administration is pursuing research to procure datasets that document how Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS)—with or without connectivity—operate in traffic. The Society of Automotive Engineers (SAE) Levels of Driving Automation Standard for Self-Driving Vehicles (J3016TM) defines ADAS and ADS features. ADAS features, which include SAE levels 1 and 2, are available in new cars sold today and include features like automatic emergency braking and adaptive cruise control. ADS technology, SAE levels 3–5, is currently under development and is being tested on roadways and test tracks today.

These datasets shall also document how adjacent vehicles in the traffic stream, detectable by vehicle sensors equipped on the ADAS/ADS, behave in the presence of the subject ADAS/ADS. These adjacent vehicles may be manually driven (SAE L0) or may have some automated capability (SAE L1–L5). These datasets, collected on arterials or freeways, shall include data variables for both the instrumented ADAS/ADS and the adjacent vehicles (manually driven, ADAS, or ADS) in the traffic stream. These datasets shall be research-ready and not requiring additional post-processing, upon delivery to FHWA, to build models of both ADAS/ADS driving behavior and the behavior of vehicles (manually driven, ADAS, and ADS) in the presence of subject ADAS/ADS. These datasets shall have clear data ownership rights, enabling FHWA to publish the datasets for public access and use, excluding proprietary and personally identifiable information (PII).

These datasets may be currently in existence, where the supplied funding will pay for the Offeror to clean, integrate, organize, and document the data, or the Offeror may propose a brand-new data collection effort for funding consideration.    

The primary technical objective of this effort is to procure vehicle trajectory datasets. Depending on the maturity of the dataset (i.e., previously collected vs. proposed to be collected), this could require several different labor tasks for the Offeror including, but not limited to:

• Developing a data management plan (DMP)
• Collecting data (if not previously collected).
• Securing data rights.
• Cleaning data (e.g., smoothing noisy data).
• Post-processing data (e.g., estimating required data variables from directly measured data).
• Scrubbing data (e.g., removing PII and proprietary data that cannot be shared publicly).
• Documenting data (e.g., consistent data format, documenting necessary metadata for the ITS DataHub).
• Estimating the accuracy of data variables.