National Surface Transportation Safety Center for Excellence Reports (NSTSCE, VTTI)
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Browsing National Surface Transportation Safety Center for Excellence Reports (NSTSCE, VTTI) by Subject "advanced driver assistance systems"
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- In-depth Analysis of Crash Risk Associated with Eyes-off-road Duration by Road Control Type and Intersection TypeHan, Shu; Guo, Feng; Klauer, Charlie (National Surface Transportation Safety Center for Excellence, 2023-01-27)This study quantified the odds ratios (ORs) associated with eyes-off-road (EOR) durations on different road control and intersection types using the Second Strategic Highway Research Program Naturalistic Data Study (SHRP 2 NDS) dataset. The motivation of this project was to provide support for driver state monitoring systems (DMSs) regarding alert timer settings when drivers look away from the road. The main research questions addressed are: (1) Should there be a different DMS alert timer setting on controlled access roads vs. uncontrolled access roads? And (2) Should there be a different alert timer setting on intersections vs. non-intersections? It is not surprising that the longer drivers look away, the higher the resulting ORs. Overall, this study suggests that different DMS alert timer settings are needed for different road geometrical characteristics. For uncontrolled access roads, a timer with a lower threshold is recommended, and a higher threshold is recommended for controlled access roads. For intersections, a zero tolerance for vision interruption is ideal. But practically, a relative lower threshold is recommended at intersections compared with non-intersection related segments. This finding could provide critical information for advanced driver assistance system development and driver behavior education programs.
- Level 2 Automated Driving Systems: Market Inventory and Development of a Reference GuideWalters, Jacob (National Surface Transportation Safety Center for Excellence, 2024-06-14)This study was a comprehensive research initiative focused on original equipment manufacturers (OEMs) with significant market shares of Level 2 (L2) automation features in model year 2022 and beyond vehicles. The primary goal of this research was to analyze and categorize operating constraints and human-machine interface (HMI) implementations associated with L2 advanced driver assistance systems (ADAS), emphasizing complex functions and interactivity. The research also prioritized understanding the nuances in implementation across different OEMs, particularly within features like adaptive cruise control and lane-keeping technologies. This assessment focused on identifying and prioritizing OEMs with significant market shares and on-road presence of L2 automation features, streamlining the scope to vehicles with immediate impact. L2 ADAS features were emphasized, particularly adaptive cruise control and lane-keeping technologies, to understand their operational complexity and nuanced HMI components. HMI interactions were categorized across sensory modalities—visual, auditory, and haptic—encompassing all forms of feedback. Describing L2 ADAS features and their communication through HMIs was a key component, alongside creating an OEM matrix outlining feature implementations and conducting cross-OEM comparisons. The matrix is a living documented resource, with the intention that it will be continuously updated with new information, serving as a comprehensive reference for L2 automation features and HMIs. Lessons learned underscore the need for deeper exploration given the variance in OEM approaches and potential pandemic-related supply chain impacts at the time of the initial data collection phase. This research initiative aims to illuminate the landscape of L2 automation features and their intricate HMI interactions, ultimately contributing to a better understanding of these technologies for both internal and external stakeholders.
- VTTI L2 Naturalistic Driving Study: A Self-funded Effort to Capture L2 Feature Use LandscapeHankey, Jonathan M.; Bedwell, Kaitlyn; Wiersma, Ethel; Stulce, Kelly E.; Perez, Miguel A. (2024-06-20)The 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.