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 "ADAS"
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- Assessing the Impact of Disability on Drivers’ Equitable Use of Advanced Driver Assistance Systems (ADAS): A Literature ReviewStulce, Kelly E.; Antin, Jonathan F. (NSTSCE, 2024-08-22)The growing prevalence of advanced driver assistance systems (ADAS) in the U.S. passenger fleet promises increased mobility and enhanced safety outcomes for all drivers, but particularly for disabled drivers, a group that comprises 11.9% of the driving population (U.S. Bureau of Labor Statistics, 2021). For ADAS to realize their full potential, stakeholders need to consider the difficulties associated with ADAS use by disabled drivers as well as the potential benefits. To support this reckoning, the authors reviewed the extant literature to discover emerging themes and to identify gaps in the literature. We then synthesized these results into a proposed road map for future work that addresses the challenges of using ADAS to enhance mobility and improve safety for all drivers, including those who are disabled. Our review of the literature reveals gaps that point the way forward for further work that will support the optimal implementation of ADAS to compensate for disability-induced driving performance deficits. Specifically, our gap analysis and research road map suggest that this work should begin with using subjective methodologies (e.g., focus groups, interviews, and surveys) to learn from the disabled driver community in a manner that centers these individuals. Such research should yield results that more authentically capture the experience (or lack thereof) of disabled individuals driving with and making use of ADAS. Additionally, longitudinal research is necessary to support extended observation of real-world ADAS use by disabled drivers across driving environments and their disability-related functional states, which are often transient
- Investigation of ADAS/ADS Sensor and System Response to Rainfall RateCowan, Jonathan B.; Stowe, Loren (National Surface Transportation Safety Center for Excellence, 2024-08-23)Advanced driver assistance systems (ADAS) and automated driving systems (ADS) rely on a variety of sensors to detect objects in the driving environment. It is well known that rain has a negative effect on sensors, whether by distorting the inputs via water film on the sensor or attenuating the signals during transmission. However, there is little research under controlled and dynamic test conditions exploring how rainfall rate affects sensor performance. Understanding how precipitation may affect the sensor’s performance, in particular the detection and state estimation performance, is necessary for safe operation of the ADAS/ADS. This research strove to characterize how rainfall rate affects sensor performance and to provide insight into the effect it may have on overall system performance. The team selected a forward collision warning/automatic emergency braking scenario with a vehicle and surrogate vulnerable road user (VRU) targets. The research was conducted on the Virginia Smart Roads’s weather simulation test area, which can generate various simulated weather conditions, including rain, across a test range of 200 m. The selected sensors included camera, lidar, and radar, which are the primary sensing modalities used in ADAS and ADS. The rain rates during testing averaged 21 mm/h and 40 mm/h. Overall, the data backed up the expected trend that increasing rainfall rate worsens detection performance. The reduced detection probability was most prominent at longer ranges, thus reducing the effective range of the sensor. The lidars showed a general linear trend of 1% reduction in range per 1 mm/h of rainfall with some target type dependence. The long-range and short-range cameras show at least a 60% reduction in detection range at 40 mm/h. The object camera, which only detected the vehicle target, showed better performance with only a 20% reduction in range at 40 mm/h, which may be due to the underlying ADAS specific detection model. For vehicles, the radars typically showed a linear drop in detection range performance with an approximately 20% reduction in range at 40 mm/h rainfall rate. The VRU target showed a larger decrease in detection range compared to the vehicle target due likely to the smaller overall signal the VRU target returns.
- 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.
- Preparing First Responder Stakeholders for ADAS and ADS DeploymentsTrimble, Tammy E.; Faulkner, Daniel (National Surface Transportation Safety Center for Excellence, 2024-12-16)Previous research has found that public safety providers are unclear about the capabilities associated with advanced driver assistance systems (ADAS)- and Automated Driving System (ADS)-related technologies. Providing outreach to this population will reduce uncertainty regarding these technologies, which in turn will lead to improved safety and interactions, including crash documentation, while in the field. A training curriculum was developed that consisted of two parts: (1) a classroom portion which can be delivered in-person or online and (2) a hands-on experiential portion. Two training options were presented to local agencies: (1) an approximately 1-hour online session, to be held at the agency’s convenience, which covers the prepared training materials; and (2) an in-person, half-day session which covers the prepared training materials and provides exposure to ADAS- and ADS-equipped vehicles. Recruitment efforts resulted in five in-person and six online attendees. In-person attendees represented three separate organizations, with one organization being represented by officers from three locations. The online attendees represented six separate organizations. Only one organization had an attendee in both the in-person and online options. To better understand the time to be allotted for the online training, the in-person training was held first. As a result, the online training was ultimately extended to 1.5 to 2 hours, which allowed time for discussion throughout the training. Feedback received directly from the participants at the conclusion of the training and via the online questionnaires was overwhelmingly positive. Moving forward, the training materials will need to be updated on a continual basis to ensure the ongoing timeliness of information shared. To share the materials with a wider range of individuals, the training could be developed and shared in a manner like the Virginia Tech Transportation Institute’s (VTTI’s) Sharing the Road program, where VTTI representatives visit schools to provide information and hands-on encounters to promote safely sharing the road with large trucks. A key to success will be employing individuals with first responder experience to provide the training. Feedback suggested that those with hands-on experience combined with their ties to VTTI resulted in perceived credibility. Also, providing hands-on opportunities to see variations in technologies across vehicle models and applications was considered beneficial. Working with VTTI partners, it may be possible to obtain demonstration vehicles for this purpose. Through this development process, the team can work towards accreditation and providing the training as part of academy, in-service, or regional training days.
- 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.