Hankey, Jonathan M.Bedwell, KaitlynWiersma, EthelStulce, Kelly E.Perez, Miguel A.2024-06-202024-06-202024-06-20https://hdl.handle.net/10919/119478The introduction of advanced driver assistance systems (ADAS) into the vehicle fleet continues to accelerate. In the past few years, that introduction has started to permeate the non-luxury vehicle segment, greatly increasing the availability of these technologies to a wide segment of the driving population. The implementation and capabilities of these systems, however, can vary widely across vehicle makes and models, which makes it imperative to have recent data that supports the study of driver adaptations in response to ADAS. While this data can take several forms, naturalistic driving data has proven to provide a flexible means of assessing real-world driver and system performance across a variety of domains and is well suited to understanding ADAS usage. The main objective of the VTTI L2 NDS data collection effort was to create a robust naturalistic driving dataset containing critical information about vehicles with ADAS. As ADAS continue to rapidly evolve and become more readily available in the vehicle fleet, it is essential to understand how these systems are being used and, in some instances misused, by drivers. This knowledge will facilitate the understanding of the safety, performance, and convenience benefits that these systems may offer drivers, along with unintended consequences from the use of these systems. The VTTI L2 NDS is available to help address a wide array of research questions that pertain to the usage of ADAS, along with traditional queries suited to NDS data, in a relatively modern fleet.application/pdfenCC0 1.0 UniversalNDSnaturalistic driving studyadvanced driver assistance systemsADASL2 driving automationLevel 2 automationnaturalistic driving datasetsReport