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Browsing by Author "Walters, Jacob"

Now showing 1 - 4 of 4
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    Exploratory Development of Algorithms for Determining Driver Attention Status
    Herbers, Eileen; Miller, Marty; Neurauter, Luke; Walters, Jacob; Glaser, Daniel (SAGE, 2023-09)
    Objective: Varying driver distraction algorithms were developed using vehicle kinematics and driver gaze data obtained from a camera-based driver monitoring system (DMS). Background: Distracted driving characteristics can be difficult to accurately detect due to wide variation in driver behavior across driving environments. The growing availability of information about drivers and their involvement in the driving task increases the opportunity for accurately recognizing attention state. Method: A baseline for driver distraction levels was developed using a video feed of 24 separate drivers in varying naturalistic driving conditions. This initial assessment was used to develop four buffer-based algorithms that aimed to determine a driver's real-time attentiveness, via a variety of metrics and combinations thereof. Results: Of those tested, the optimal algorithm included ungrouped glance locations and speed. Notably, as an algorithm's performance of detecting very distracted drivers improved, its accuracy for correctly identifying attentive drivers decreased. Conclusion: At a minimum, drivers' gaze position and vehicle speed should be included when designing driver distraction algorithms to delineate between glance patterns observed at high and low speeds. Distraction algorithms should be designed with an understanding of their limitations, including instances in which they may fail to detect distracted drivers, or falsely notify attentive drivers. Application: This research adds to the body of knowledge related to driver distraction and contributes to available methods to potentially address and reduce occurrences. Machine learning algorithms can build on the data elements discussed to increase distraction detection accuracy using robust artificial intelligence.
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    Improving Methods to Measure Attentiveness through Driver Monitoring
    Miller, Marty; Herbers, Eileen; Walters, Jacob; Neurauter, Luke (SAFE-D: Safety Through Disruption National University Transportation Center, 2022-07)
    Driver inattention poses a significant problem on today’s roadways, increasing risk for all road users. This report details our efforts in developing algorithms to detect driver inattention. A benchmark dataset was developed based on video review of driving events. Buffer-based algorithms were developed and compared using this benchmark dataset. The benchmark events were also used as a training dataset for machine learning models. Driver glance locations were important for determining driver attentiveness. In addition, vehicle speed was important for understanding the driving context, which was found to have a large impact on driver behavior.
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    Level 2 Automated Driving Systems: Market Inventory and Development of a Reference Guide
    Walters, 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.
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    Quiet Car Detectability: Impact of Artificial Noise on Ability of Pedestrians to Safely Detect Approaching Electric Vehicles
    Neurauter, Luke; Roan, Michael J.; Song, Miao; Miller, Marty; Glenn, Eric; Walters, Jacob (National Surface Transportation Safety Center for Excellence, 2020-04-10)
    Many auto manufacturers are now producing hybrid and electric vehicles with an additive noise component to signal vehicle presence in the same way that internal combustion engine vehicles signal their presence through engine noise. The Virginia Tech Transportation Institute conducted an evaluation of quiet car detectability as part of a GM-funded project in 2015–2016. The internal combustion engine benchmark significantly outperformed the other three vehicles under a 10-km/h steady approach, but these differences largely disappeared at 20 km/h due to increased tire and road noise. Trends of improved detectability offered by the additive noise signals were observed but did not demonstrate a significant advantage over an electric vehicle with no additional noise component. Since that original project, NHTSA has released their final version of Federal Motor Vehicle Safety Standard (FMVSS) 141, outlining “Minimum Sound Requirements for Hybrid and Electric Vehicles.” This project aimed to demonstrate differences in detectability by replicating the previous study but with newer FMVSS 141-compliant sounds. The proposed additive sounds examined drastically improved detectability compared to the production variants included in the first round of testing. At 10 km/h, the additive sound conditions outperformed the no-sound condition by magnitudes ranging from 3.4 to 4.6, each eliciting mean detection distances well above the NHTSA minimum detection criteria. At 20 km/h, detectability also improved dramatically over the earlier production variants, achieving a similar magnitude advantage over no-sound as observed at 10 km/h. Increasing background noise resulted in a measurable impact on mean detection distances. The average reduction across all conditions was approximately 33% and 28% for approach speeds of 10 km/h and 20 km/h, respectively. In terms of accurately recognizing a stopped vehicle in a 20 to 0 km/h scenario, all sound conditions significantly outperformed the no-sound condition across both background noise conditions.