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 Content Type "Technical Report"
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- Human-Machine Interface Review: A Comparison of Legacy and Touch-Based Center Stack ControlsAnderson, Gabrial T.; Antona-Makoshi, Jacobo; Klauer, Charlie (National Surface Transportation Safety Center for Excellence, 2024-04-19)The current study investigated the effect of center stack design on driver distraction. Replacing physical center stack controls with touchscreens is an emerging trend in automotive design. This design decision requires a driver to take their eyes off the forward roadway to interact with a touchscreen center stack, as there is no tactile feedback like touching physical controls. Multiple resource theory (Wickens, 2004) suggests that performing dual tasks (i.e., driving and touchscreen interaction) that compete for similar resources (i.e., visual attention and manual input) can degrade performance on both tasks. It is important to understand the impact of touchscreen controls on driver distraction to ensure safe human-machine interface design. Data from legacy vehicles with physical center stack controls were extracted from the Second Strategic Highway Research Program, an NDS focusing on driver behavior over time in personal vehicles. Data from modern vehicles with touchscreen designs were extracted from the Virginia Connected Corridor 50 Elite Vehicle NDS and Virginia Tech Transportation Institute Level 2 NDS, both focusing on driver behavior in personal vehicles equipped with SAE Level 2 (L2) driving automation features. Twenty-second events that had a center stack interaction (CSI) and minimum speed of 35-mph or greater were selected from each dataset. For the modern vehicle dataset, L2 system status was coded for each event as L2 active or L2 inactive, and task type was coded as visual or visual-manual. The legacy vehicle dataset only had visual-manual CSIs. Driver distraction was defined as eye glances towards the center stack (eyes on center stack; EOCS) during the 20-second event. EOCS was split into total time, mean time, single longest glance, number of glances, and glances over 2 seconds in duration. Total time on task was recorded for the modern vehicles. Results suggest that CSIs with modern vehicle touchscreens have higher EOCS compared to legacy vehicle physical controls. Notably, these differences are even more pronounced when comparing visual-manual CSIs (e.g., adjusting climate control) across display type. Modern vehicle CSIs were also more likely to include glances over 2 seconds compared to legacy vehicle CSIs. Within the modern vehicle dataset, all EOCS metrics (except number of glances), time on task, and glances over 2 seconds were significantly higher when L2 systems were active versus inactive. Visual-manual CSIs were higher for all variables compared to visual CSIs. Glances over 2 seconds were more likely when L2 systems were active for all visual CSIs, but not for visual-manual CSIs. Touchscreen center stack designs are shown to be more distracting than legacy designs comprised of physical controls. When L2 systems are active, CSIs are more distracting than when L2 systems are inactive. Although display type has been shown to have a distracting effect, comparison of specific tasks (e.g., adjusting climate controls) is needed to represent true differences in driver distraction, as more complex tasks that are possible in modern vehicles versus legacy vehicles could contribute to the results of the current study.
- 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.
- Real-time Risk Prediction Using Temporal Gaze Pattern and Evidence AccumulationYang, Gary; Kaskar, Omkar; Sarkar, Abhijit (National Surface Transportation Safety Center for Excellence, 2024-06-26)Driver gaze information provides insight into the driver’s perception and decision-making process in a dynamic driving environment. It is important to learn whether driver gaze information immediately before a safety-critical event (SCE) can be used to assess crash risk, and if so, how early in the crash we can predict the potential crash risk. This requires a thorough understanding of the temporal pattern of the data. In this project, we explored multiple key research questions pertaining to driver gaze and its relation to SCEs. We first looked at how temporal gaze patterns in SCEs like crashes and near-crashes are different from baseline events. Then we extended this analysis to understand if the temporal gaze pattern can indicate the direction of the threat. Finally, we showed how contextual information can aid the understanding of gaze patterns. We used traffic density, positions of traffic actors, and vehicle speed as key factors that can influence the temporal nature of gaze and impact safety. This study introduces a real-time crash risk prediction framework leveraging deep learning models based on driver gaze and contextual data. The analysis utilizes the Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study, preprocessing it to extract temporal driver gaze patterns and contextual data for two distinct subsets of data. Dataset 1 comprises one-dimensional temporal driver gaze patterns only (named “SHRP2_Gaze”). Dataset 2 includes additional contextual information such as vehicle speed, distance to lead vehicle, and level of service (named “SHRP2_Gaze_Context).” Dataset 1 was used to explore the feasibility of predicting crash risk solely based on driver gaze data. Dataset 2 was applied to assessing the potential for early crash warnings by analyzing both driver gaze patterns and contextual data. For SHRP2_Gaze, four deep learning models (long short-term memory [LSTM], one-dimensional convolutional neural network [1D CNN], 1D CNN+LSTM, and 1D CNN+LSTM+Attention) were trained and tested on various inputs (Input 1: 20 seconds from five zones; Input 2: different lengths from five zones; and Input 3: 20 seconds from 19 zones), considering different lengths and zones of driver gaze data. The 1D CNN with XGBoost exhibited superior performance in crash risk prediction but demonstrated challenges in distinguishing specific crash and near-crash events. This portion of the study emphasized the significance of the temporal gaze pattern length and zone selection for real-time crash risk prediction using driver gaze data only. With SHRP2_Gaze_Context, the 1D CNN model with XGBoost emerged as the top-performing model when utilizing 11 types of 20-second driver gaze and contextual data to predict crash, near-crash, and baseline events. Notably, vehicle speed proves highly correlated with event categories, showcasing the effectiveness of combining driver gaze and contextual data. The study also highlighted the model’s ability to adapt to early crash warning scenarios, leveraging different 2-second multivariate data without an increase in computing time. The deep learning models employed in this study outperform traditional statistical models in distinguishing features from driver gaze and contextual data. These findings hold significant implications for accurate and efficient real-time crash risk prediction and early crash warning systems. The study’s insights cater to public agencies, insurance companies, and fleets, providing valuable understanding of driver behavior and contextual information leading to diverse safety events.
- Rocky Mountain Naturalistic Driving StudyDunn, Naomi; Viray, Reginald (National Surface Transportation Safety Center for Excellence, 2024-06-17)The Rocky Mountain Naturalistic Driving Study (RMNDS) was, at the time of data collection, the first attempt to use the NDS methodology to conduct research on the effects of cannabis on driving performance. The resulting dataset comprises over 14,000 trips made by 23 participants who self-reported medium to heavy cannabis use and who also reported they had a history of driving under the influence of cannabis. A unique aspect of the study was the collection of quantitative and qualitative drug use data. Qualitative drug use data was collected via an online journal, while quantitative drug use data was collected using a Quantisal oral fluid collection device prior to one driving trip each week. Samples were sent to a toxicology laboratory for analysis, which produced quantifiable test results for the National Institute on Drug Abuse 5 drug panel, including delta-9-THC. Out of the 14,000+ trips, there were a total of 178 verified drug test results along with 1,549 drug use journal entries. While the study proved successful for collecting naturalistic driving data and both objective and subjective drug use data, the difficulty comes in linking the drug use data to the driving data in order to identify periods of driving that may be impacted by the consumption of cannabis and/or other drugs. Further analysis of the RMNDS data, including identification of trips linked to drug use, would provide invaluable information about the impact of cannabis and/or other drugs on driving performance.
- Truck Driver Compensation and Crash RiskGuo, Feng; Xiang, Lanxin (National Surface Transportation Safety Center for Excellence, 2024-06-17)Compensation methods might affect commercial drivers’ behavior and subsequently the possibility of being involved in a crash. This study investigated the relationship between various compensation strategies for commercial truck drivers and their associated risk of involvement in crashes. A nested case-control approach was adopted. Compensation data was sourced from the follow-up survey of the Commercial Driver Safety Risk Factors study. Upon a case-crash being identified, up to five consented non-crash drivers were contacted to complete a follow-up survey. The analysis employed standard logistic regression and two conditional logistic regression (CLR) models. The CLR models were stratified based on the date of the crash (reference date) and the years of commercial vehicle driving experience to control for temporal and experience-related factors that could confound the relationship between compensation methods and crash risk. To adjust for multiple comparison issues, Tukey’s tests were employed. The results indicate a significant difference in crash rate among driver compensation types. Drivers in PayPerMile-PayPerLoad are associated with a higher crash risk compared to PayPerHour, with odds ratios (ORs) of 3.512, 2.304, and 2.853, in three models, respectively (p < 0.05). Similarly, PayPerMile-PayPerTrip showed increased risk compared to PayPerHour, with ORs of 3.207, 2.260, and 2.548, respectively (p < 0.05). Tukey’s tests confirm significant differences among compensation methods, particularly between PayPerMile-PayPerLoad and PayPerHour, PayPerMile-PayPerLoad, and PayPerLoad, and within PayPerMile-PayPerLoad and PayPerMile. The study suggests a potential link between compensation methods and crash risk, highlighting the need for further research to pinpoint which compensation strategies significantly affect driving safety.