Visual function correlates of self-reported vision-related nighttime driving difficulties / La función visual se correlaciona con las dificultades de conducción nocturna relacionadas con la visión autoinformadas

Purpose: To investigate the visual function correlates of self-reported vision-related night driving difficulties among drivers. Methods: One hundred and seven drivers (age: 46.06 ± 8.24, visual acuity [VA] of 0.2logMAR or better) were included in the study. A standard vision and night driving questionnaire (VND-Q) was administered. VA and contrast sensitivity were measured under photopic and mesopic conditions. Mesopic VA was remeasured after introducing a peripheral glare source into the participants' field of view to enable computation of disability glare index. Regression analyses were used to assess the associations between VND-Q scores, and visual function measures. Results: The mean VND-Q score was -3.96±1.95 logit (interval scale score: 2.46±1.28). Simple linear regression models for photopic contrast sensitivity, mesopic VA, mesopic contrast sensitivity, and disability index significantly predicted VND-Q score (P<0.05), with mesopic VA and disability glare index accounting for the greatest variation (21 %) in VND-Q scores followed by photopic contrast sensitivity (19 %), and mesopic contrast sensitivity (15 %). A multiple regression model to determine the association between the predictors (photopic contrast sensitivity, mesopic VA, mesopic contrast sensitivity, and disability index) and VND-Q score yielded significant results, F (4, 102) = 8.58, P < 0.001, adj. R2 = 0.2224. Seeing dark-colored cars was the most challenging vision task. Conclusion: Changes in mesopic visual acuity, photopic and mesopic contrast sensitivity, as well as disability glare index are associated with and explain night driving-related visual difficulties. It is recommended to incorporate measurement of these visual functions into assessments related to driving performance.(AU)

A comprehensive evaluation of the frog effect on drivers in mountain highway tunnels - The effect of low-volume intermittent information.

OBJECTIVE: The driver's inability to fully absorb and react to operational cues while driving is like boiling a frog in warm water. With intermittent, low-volume information, drivers can underreact by ignoring these minor but continuous changes. This paper aims to provide an opportunity to test the effects of intermittently occurring low-volume information on drivers. METHODS: A real vehicle test with naturalistic driving was used to collect driving speed data from 40 drivers on a highway tunnel section in Chongqing, China, where nine tunnels are located. Drivers were classified into three categories according to the degree of compliance of their driving speed with the speed limit required by traffic signs, and drivers were analyzed in terms of their sensitivity to traffic signs and their reaction to driving maneuvers. RESULTS: Conservative drivers are the most absorbent of low-volume intermittent information, and the cumulative effect of the frog effect does not exceed 2.00 km; eager drivers tend to ignore this information, and the cumulative effect of the frog effect reaches 2.91 km; and the general type of driver is in the middle of these two types of drivers, and the frog effect gradually penetrates the driving speed in a weakly increasing manner, up to a maximum of 9.8 km. CONCLUSION: At the beginning of a journey, drivers are most sensitive to traffic signs, and low-volume intermittent information can also play a role in guiding driving operations effectively at this time. However, as the driving distance increases, the effect of the frog effect on different types of drivers gradually increases, even exceeding the effect caused by the black-and-white hole effect, especially when driving in tunnel groups. Considering the driving characteristics of different types of drivers to improve the deployment of low-volume intermittent information and reduce the distance of the frog effect can effectively improve driving safety.

Customizable Colorimetric Sensor Array via a High-Throughput Robot for Mitigation of Humidity Interference in Gas Sensing.

One challenge for gas sensors is humidity interference, as dynamic humidity conditions can cause unpredictable fluctuations in the response signal to analytes, increasing quantitative detection errors. Here, we introduce a concept: Select humidity sensors from a pool to compensate for the humidity signal for each gas sensor. In contrast to traditional methods that extremely suppress the humidity response, the sensor pool allows for more accurate gas quantification across a broader range of application scenarios by supplying customized, high-dimensional humidity response data as extrinsic compensation. As a proof-of-concept, mitigation of humidity interference in colorimetric gas quantification was achieved in three steps. First, across a ten-dimensional variable space, an algorithm-driven high-throughput experimental robot discovered multiple local optimum regions where colorimetric humidity sensing formulations exhibited high evaluations on sensitivity, reversibility, response time, and color change extent for 10-90% relative humidity (RH) in room temperature (25 °C). Second, from the local optimum regions, 91 sensing formulations with diverse variables were selected to construct a parent colorimetric humidity sensor array as the sensor pool for humidity signal compensation. Third, the quasi-optimal sensor subarrays were identified as customized humidity signal compensation solutions for different gas sensing scenarios across an approximately full dynamic range of humidity (10-90% RH) using an ingenious combination optimization strategy, and two accurate quantitative detections were attained: one with a mean absolute percentage error (MAPE) reduction from 4.4 to 0.75% and the other from 5.48 to 1.37%. Moreover, the parent sensor array's excellent humidity selectivity was validated against 10 gases. This work demonstrates the feasibility and superiority of robot-assisted construction of a customizable parent colorimetric sensor array to mitigate humidity interference in gas quantification.

Analysis of dynamic evolution characteristics and driving factors of regional synergistic effect of reducing carbon and saving water: the case of Yangtze River Delta Urban Agglomerations, China.

Carbon emissions and water consumption are both important factors affecting sustainable development. Therefore, it is necessary to put them in the same research framework and investigate the synergy. In this study, the dynamic evolution characteristics of the synergistic effect of reducing carbon and saving water (RCSW) were analyzed. Then, taking the Yangtze River Delta Urban Agglomerations (YRDUA) as the research object, the influencing factors and specific paths of the synergistic effect were clarified. The results showed that the low-carbon emission efficiency (LCEE) had a stable synergy with the intensive utilization efficiency of water resources (IUEWR) in the YRDUA. Government financial expenditure, actual use of foreign capital, and population density were the most significant driving forces for the synergistic effect of RCSW, with q values of 0.561, 0.363, and 0.240, respectively. In addition, most of the interactions of the driving factors were nonlinear enhancement and double-factor enhancement.

General theory for localizing the where and when of entropy production meets single-molecule experiments.

The laws of thermodynamics apply to biophysical systems on the nanoscale as described by the framework of stochastic thermodynamics. This theory provides universal, exact relations for quantities like work, which have been verified in experiments where a fully resolved description allows direct access to such quantities. Complementary studies consider partially hidden, coarse-grained descriptions, in which the mean entropy production typically is not directly accessible but can be bounded in terms of observable quantities. Going beyond the mean, we introduce a fluctuating entropy production that applies to individual trajectories in a coarse-grained description under time-dependent driving. Thus, this concept is applicable to the broad and experimentally significant class of driven systems in which not all relevant states can be resolved. We provide a paradigmatic example by studying an experimentally verified protein unfolding process. As a consequence, the entire distribution of the coarse-grained entropy production rather than merely its mean retains spatial and temporal information about the microscopic process. In particular, we obtain a bound on the distribution of the physical entropy production of individual unfolding events.

Impact of sidewall information on drivers' visual load among different lanes: Traditional guiding facilities and decorated pattern.

OBJECTIVES: This research aims to: (i) compare the effects of different sidewall entrainment facilities on drivers' visual behavior; (ii) compare the effects of the same sight entrainment facilities on drivers in different lanes; (iii) give recommendations for engineering applications based on the results of the study. METHODS: The study designed four different scenes, each with symmetrically designed visual facilities on the both sidewalls of the tunnel, scene a represents a typical urban tunnel in China (horizontal stripes on sidewalls), scene b includes vertical stripes on sidewalls in addition to scene a, scene c introduces an LED-arch based on scene b, and scene d features a rhythmic pattern (Wave pattern on sidewalls). 30 participants, 21 men and 9 women, aged 21-54, drove the four scenes. Eye movement data of participants in each lane for different scenes were collected using an eye-tracking device. Visual performance indicators including fixation duration, number of fixations, saccade duration, and saccade amplitude were utilized to comprehensively evaluate drivers' visual behavior. Factor analysis was employed to analyze the impact of different visual guiding facilities on drivers' visual searching abilities. RESULTS: There is a significant effect of sidewall guiding facilities and lane location on drivers' visual behavior and loading. Across scenes, drivers' visual load is ranked as follows, from highest to lowest: scene a (baseline) > scene b (horizontal stripes added to scene a) > scene c (LED-arch added to scene b) > scene d (Wave pattern). Furthermore, under the same scene, drivers' visual load in each lane is ranked in descending order: Middle lane > Right lane > Left lane. CONCLUSION: Due to the effect of the tunnel structure on the drivers' visual field, drivers in the left lane have the highest visual load in any scenario compared to the other two lanes, which can be ameliorated but not eliminated. Traditional guiding facilities and decorated pattern both improve the visual behavior and reduce drivers' visual load in urban tunnels, especially in scene c and scene d, but scene d should not be used for the entire length of the tunnel in order to prevent driver distraction. In engineering practice, scene c (LED-arch added to scene b) can be set up in general sections of urban tunnels, and decorated pattern can be added to fatigue reminder regions to alleviate driving fatigue.

Investigating the influence of eye-catching effect on mental workload in highway tunnel entrances: a comprehensive analysis of eye blink behavior.

OBJECTIVE: The objective of this study was to investigate the relationship between eye-catching effects and mental workload at highway tunnel entrances. Specifically, the study aimed to analyze drivers' eye blink behavior to gain a comprehensive understanding of how visual attraction at tunnel entrances affects cognitive workload. METHODS: 50 participants were recruited for the naturalistic driving experiment. Four different visually attractive driving scenarios (baseline, landscape-style architecture, tip slogan, and billboard) were selected. Eye-tracking technology was utilized to record and analyze the eye blink behavior of participating drivers. Various metrics, including blink frequency, blink duration, inter-blink interval, and pupil diameter after a blink, were measured and compared across different scenarios. RESULTS: The results of the study demonstrated significant differences in drivers' eye blink behavior across the different experimental scenarios, indicating the influence of visual attraction conditions on mental workload. The presence of eye-catching stimuli (landscape-style architecture, tip slogan, and billboard scenarios) at tunnel entrances resulted in decreased blink frequency, shorter blink duration, longer inter-blink intervals, and larger pupil diameter after a blink compared to when no specific eye-catching stimuli were present (baseline condition). These findings suggest that visual attractions capture drivers' attention, leading to increased cognitive workload and attentional demands. CONCLUSIONS: The findings of this study contribute to the existing literature on driver attention and mental workload, particularly in relation to eye-catching effect in tunnel environments. The presence of eye-catching stimuli at tunnel entrances can distract drivers and increase their mental workload, potentially compromising driving performance and safety. It is crucial for transportation authorities and designers to carefully consider the design and placement of visual attractions in tunnel entrances to minimize distraction and cognitive workload. By doing so, driving safety and performance can be enhanced in tunnel entrances.

Young onset dementia and driving cessation: a scoping review of lived experiences.

BACKGROUND: Driving cessation is one of the most challenging life transitions, associated with multiple negative consequences for individuals living with late-onset dementia. This paper extends the literature as to date there is no published review that details the experiences of people living with young onset dementia ("YOD"). METHODS: A comprehensive search of the literature was conducted using the scoping review methodology. RESULTS: Ten studies were included for full text review of 1634 initially identified through database searching. The results of the included articles indicated areas of concern for people living with YOD and their family members including, loss of independence; role change; threat to self-identify; feelings of isolation, grief; acceptance; predictors of driving cessation. CONCLUSION: There is a lack of robust evidence related to driving cessation and the experiences of people living with YOD. No published paper reported psychosocial interventions specifically targeted at supporting persons with YOD through driving cessation.

Toward Safe and Confident Silver Drivers: Interview Study Investigating Older Adults' Driving Practices.

BACKGROUND: As the aging population in the United States continues to increase rapidly, preserving the mobility and independence of older adults becomes increasingly critical for enabling aging in place successfully. While personal vehicular transport remains a popular choice among this demographic due to its provision of independence and control over their lives, age-related changes may heighten the risk of common driving errors and diminish driving abilities. OBJECTIVE: This study aims to investigate the driving practices of older adults and their efforts to maintain safe and confident driving habits. Specifically, we sought to identify the factors that positively and negatively influence older adults' driving performance and confidence, as well as the existing efforts put into sustaining their driving abilities. METHODS: We recruited 20 adults aged ≥65 years who remained active drivers during the recruitment from the greater New York area. Then, we conducted semistructured interviews with them to examine their perceptions, needs, and challenges regarding safe and confident driving. RESULTS: Our findings uncovered a notable disparity between older adults' self-perceived driving skills and the challenges they face, particularly caused by age-related limitations and health conditions such as vision and memory declines and medication routines. Drawing on these findings, we proposed strategies to bridge this gap and empower older adults to drive safely and confidently, including fostering a realistic understanding of their capabilities, encouraging open dialogue regarding their driving, encouraging regular assessments, and increasing awareness of available resources. CONCLUSIONS: This study uncovered a noticeable disparity between the perceived driving competence of older adults and the actual challenges they confront while driving. This divergence underscores a significant need for better support beyond the existing aid available to preserve older adults' driving skills. We hope that our recommendations will offer valuable insights for practitioners and scholars committed to enhancing the overall well-being and quality of life for older adults as they age in their homes.

First North American Experience with the Berlin Heart EXCOR® Active Driver.

For smaller pediatric patients on ventricular assist devices (VADs), the Berlin Heart EXCOR® remains the main form of durable support. It requires a connection to the external IKUS which has limited portability and battery life. The new EXCOR® Active mobile driving unit has battery life up to 13 hours. We describe the first North American experience with the EXCOR® Active in pediatric patients with a Berlin Heart device. Retrospective chart review was undertaken. Between Oct/2022 to Mar/2024, seven patients were on a Berlin Heart and supported with the EXCOR® Active. All patients were initially supported with the IKUS with a median time to transition to the EXCOR® Active of 12.0 days (IQR 9.5, 18.5) and a median time of support with the EXCOR® Active of 65.0 days (IQR, 32.0, 81.0). The EXCOR® Active posed no significant safety issues and minimal operating issues were noted. Following transition from IKUS to the EXCOR® Active there was increased patient and caregiver mobility throughout the hospital. Use of the EXCOR® Active has the potential to improve quality of life in pediatric patients waiting for heart transplantation.

Exploring high-emission driving behaviors of heavy-duty diesel vehicles based on engine principles under different road grade levels.

To reveal the outstanding high-emission problems that occur when heavy-duty diesel vehicles (HDDV) pass uphill and downhill, this study proposes a method to depict the nitrogen oxides (NOx) and carbon dioxide (CO2) high-emission driving behaviors caused by slopes from the perspective of engine principles. By calculating emission and grade data of HDDV based on on-board diagnostic (OBD) data and digital elevation model (DEM) data, the 262 short trips including uphill, flat-road and downhill are firstly obtained through the rule-based short trip segmentation method, and the significant correlation between the road grade and emissions of the short trips is verified by Kendall's Tau and K-means clustering. Secondly, by comparing the distribution changes of three speed categories (acceleration state, constant speed state and deceleration state), the differences in HDDV operating states under different grade levels are discussed. Finally, the machine learning models (Random Forest, XGBoost and Elastic Net), are used to develop the NOx and CO2 emission estimation model, identifying high-emission driving behaviors, particularly during uphill driving, which showed the highest proportion of high-emission. Explained by the feature importance and SHapley Additive exPlanations (SHAP) model that large accelerator pedal opening, frequent aggressive acceleration, and high engine load have positive effects both on NOx and CO2 emissions. The difference is in the air-fuel ratio that the engine in the rich or slightly lean burning state will increase CO2 emissions and the lean burning state will increase NOx emissions. In addition, due to the uncertainty of the actual uphill, drivers often undergo a rapid "deceleration-uniform-acceleration" process, which significantly contributes to high NOx and CO2 emissions from the engine perspective. The findings provide insights for designing driving strategies in slope scenarios and offer a novel perspective on depicting driving behaviors.

RCRFNet: Enhancing Object Detection with Self-Supervised Radar-Camera Fusion and Open-Set Recognition.

Robust object detection in complex environments, poor visual conditions, and open scenarios presents significant technical challenges in autonomous driving. These challenges necessitate the development of advanced fusion methods for millimeter-wave (mmWave) radar point cloud data and visual images. To address these issues, this paper proposes a radar-camera robust fusion network (RCRFNet), which leverages self-supervised learning and open-set recognition to effectively utilise the complementary information from both sensors. Specifically, the network uses matched radar-camera data through a frustum association approach to generate self-supervised signals, enhancing network training. The integration of global and local depth consistencies between radar point clouds and visual images, along with image features, helps construct object class confidence levels for detecting unknown targets. Additionally, these techniques are combined with a multi-layer feature extraction backbone and a multimodal feature detection head to achieve robust object detection. Experiments on the nuScenes public dataset demonstrate that RCRFNet outperforms state-of-the-art (SOTA) methods, particularly in conditions of low visual visibility and when detecting unknown class objects.

Combining Optimization and Simulation for Next-Generation Off-Road Vehicle E/E Architectural Design.

The automotive industry, with particular reference to the off-road sector, is facing several challenges, including the integration of Advanced Driver Assistance Systems (ADASs), the introduction of autonomous driving capabilities, and system-specific requirements that are different from the traditional car market. Current vehicular electrical-electronic (E/E) architectures are unable to support the amount of data for new vehicle functionalities, requiring the transition to zonal architectures, new communication standards, and the adoption of Drive-by-Wire technologies. In this work, we propose an automated methodology for next-generation off-road vehicle E/E architectural design. Starting from the regulatory requirements, we use a MILP-based optimizer to find candidate solutions, a discrete event simulator to validate their feasibility, and an ascent-based gradient method to reformulate the constraints for the optimizer in order to converge to the final architectural solution. We evaluate the results in terms of latency, jitter, and network load, as well as provide a Pareto analysis that includes power consumption, cost, and system weight.

Overview of Radar Alignment Methods and Analysis of Radar Misalignment's Impact on Active Safety and Autonomous Systems.

The rapid development of active safety systems in the automotive industry and research in autonomous driving requires reliable, high-precision sensors that provide rich information about the surrounding environment and the behaviour of other road users. In practice, there is always some non-zero mounting misalignment, i.e., angular inaccuracy in a sensor's mounting on a vehicle. It is essential to accurately estimate and compensate for this misalignment further programmatically (in software). In the case of radars, imprecise mounting may result in incorrect/inaccurate target information, problems with the tracking algorithm, or a decrease in the power reflected from the target. Sensor misalignment should be mitigated in two ways: through the correction of an inaccurate alignment angle via the estimated value of the misalignment angle or alerting other components of the system of potential sensor degradation if the misalignment is beyond the operational range. This work analyses misalignment's influences on radar sensors and other system components. In the mathematically proven example of a vertically misaligned radar, pedestrian detectability dropped to one-third of the maximum range. In addition, mathematically derived heading estimation errors demonstrate the impact on data association in data fusion. The simulation results presented show that the angle of misalignment exponentially increases the risk of false track splitting. Additionally, the paper presents a comprehensive review of radar alignment techniques, mostly found in the patent literature, and implements a baseline algorithm, along with suggested key performance indicators (KPIs) to facilitate comparisons for other researchers.

Enhancing Driving Safety through User Experience Evaluation of the C-ITS Mobile Application: A Case Study of the DARS Traffic Plus App in a Driving Simulator Environment.

The paper evaluates the DARS Traffic Plus mobile application within a realistic driving simulator environment to assess its impact on driving safety and user experience, particularly focusing on the Cooperative Intelligent Transport Systems (C-ITS). The study is positioned within the broader context of integrating mobile technology in vehicular environments to enhance road safety by informing drivers about potential hazards in real time. A combination of experimental methods was employed, including a standardised user experience questionnaire (meCUE 2.0), measuring quantitative driving parameters and eye-tracking data within a driving simulator, and post-experiment interviews. The results indicate that the mobile application significantly improved drivers' safety perception, particularly when notifications about hazardous locations were received. Notifications displayed at the top of the mobile screen with auditory cues were deemed most effective. The study concludes that mobile applications like DARS Traffic Plus can play a crucial role in enhancing road safety by effectively communicating hazards to drivers, thereby potentially reducing road accidents and improving overall traffic safety. Screen viewing was kept below the safety threshold, affirming the app's efficacy in delivering crucial information without distraction. These findings support the integration of C-ITS functionalities into mobile applications as a means to augment older vehicle technologies and extend the safety benefits to a broader user base.

End-to-End Autonomous Driving Decision Method Based on Improved TD3 Algorithm in Complex Scenarios.

The ability to make informed decisions in complex scenarios is crucial for intelligent automotive systems. Traditional expert rules and other methods often fall short in complex contexts. Recently, reinforcement learning has garnered significant attention due to its superior decision-making capabilities. However, there exists the phenomenon of inaccurate target network estimation, which limits its decision-making ability in complex scenarios. This paper mainly focuses on the study of the underestimation phenomenon, and proposes an end-to-end autonomous driving decision-making method based on an improved TD3 algorithm. This method employs a forward camera to capture data. By introducing a new critic network to form a triple-critic structure and combining it with the target maximization operation, the underestimation problem in the TD3 algorithm is solved. Subsequently, the multi-timestep averaging method is used to address the policy instability caused by the new single critic. In addition, this paper uses Carla platform to construct multi-vehicle unprotected left turn and congested lane-center driving scenarios and verifies the algorithm. The results demonstrate that our method surpasses baseline DDPG and TD3 algorithms in aspects such as convergence speed, estimation accuracy, and policy stability.

Impact of Perception Errors in Vision-Based Detection and Tracking Pipelines on Pedestrian Trajectory Prediction in Autonomous Driving Systems.

Pedestrian trajectory prediction is crucial for developing collision avoidance algorithms in autonomous driving systems, aiming to predict the future movement of the detected pedestrians based on their past trajectories. The traditional methods for pedestrian trajectory prediction involve a sequence of tasks, including detection and tracking to gather the historical movement of the observed pedestrians. Consequently, the accuracy of trajectory prediction heavily relies on the accuracy of the detection and tracking models, making it susceptible to their performance. The prior research in trajectory prediction has mainly assessed the model performance using public datasets, which often overlook the errors originating from detection and tracking models. This oversight fails to capture the real-world scenario of inevitable detection and tracking inaccuracies. In this study, we investigate the cumulative effect of errors within integrated detection, tracking, and trajectory prediction pipelines. Through empirical analysis, we examine the errors introduced at each stage of the pipeline and assess their collective impact on the trajectory prediction accuracy. We evaluate these models across various custom datasets collected in Taiwan to provide a comprehensive assessment. Our analysis of the results derived from these integrated pipelines illuminates the significant influence of detection and tracking errors on downstream tasks, such as trajectory prediction and distance estimation.

Quantum Interference and Coherent Population Trapping in a Double Quantum Dot.

Quantum interference is a natural consequence of wave-particle duality in quantum mechanics, and is widely observed at the atomic scale. One interesting manifestation of quantum interference is coherent population trapping (CPT), first proposed in three-level driven atomic systems and observed in quantum optical experiments. Here, we demonstrate CPT in a gate-defined semiconductor double quantum dot (DQD), with some unique twists as compared to the atomic systems. Specifically, we observe CPT in both driven and nondriven situations. We further show that CPT in a driven DQD could be used to generate adiabatic state transfer. Moreover, our experiment reveals a nontrivial modulation to the CPT caused by the longitudinal driving field, yielding an odd-even effect and a tunable CPT. Our results broaden the field of CPT, and open up the possibility of quantum simulation and quantum computation based on adiabatic passage in quantum dot systems.

Soil organic carbon formation efficiency from straw/stover and manure input and its drivers: Estimates from long-term data in global croplands.

New soil organic carbon (SOC) formation in cropland from straw/stover or manure input is a vital source of SOC for climate change mitigation. However, location and variations in the efficiency, specifically the ratio of new SOC formation to organic C input (NCE), remain unquantified globally. In this study, the spatial variability of cropland NCE from straw/stover or manure input and explanatory factors were determined by analyzing 897 pairs of long-term field measurements from 404 globally distributed sites and by mapping grid-level cropland NCEs. The global NCE for paddy and upland averaged 13.8% (8.7%-25.1%, 5th-95th percentile) and 10.9% (6.8%-17.3%), respectively. The initial SOC and the clay content of soil, rather than temperature, were the most important factors regulating NCE. A parabola with an apex at approximately 17 g kg-1 between the initial SOC and NCE was resolved, and a positive correlation between soil clay content and NCE was observed. High-resolution mapping of the global NCE derived from manure/straw and insight into NCE dynamics provide a benchmark for diagnosing cropland soil C dynamics under climate change and identifying priority regions and actions for C management.

Principal Components Analysis of Driving Simulator Variables in Novice Drivers.

Objective: Although driving simulators are powerful tools capable of measuring a wide-ranging set of tactical and operational level driving behaviors, comparing these behaviors across studies is problematic because there is no core set of driving variables to report when assessing driving behavior in simulated driving scenarios. To facilitate comparisons across studies, researchers need consistency in how driving simulator variables combine to assess driving behavior. With inter-study consistency, driving simulator research could support stronger conclusions about safe driving behaviors and more reliably identify future driver training goals. The purpose of the current study was to derive empirically and theoretically meaningful composite scores from driving behaviors of young people in a driving simulator, utilizing driving data from across a variety of driving environments and from within the individual driving environments. Method: One hundred ninety adolescent participants aged 16 years or 18 years at enrollment provided demographic data and drove in a high-fidelity driving simulator. The simulated scenario included 4 distinct environments: Urban, Freeway, Residential, and a Car Following Task (CFT). A Principal Components Analysis (PCA) was conducted on the variable output from the driving simulator to select optimal factor solutions and loadings both across the multi-environmental drive and within the four individual driving environments. Results: The PCA suggested two components from the multi-environmental simulated drive: vehicle control and speed. The individual driving environments also indicated two components: vehicle control and tactical judgment. Conclusion: These findings are among the first steps for identifying composite driving simulator variables to quantify theoretical conceptualizations of driving behavior. Currently, driving behavior and performance measured by driving simulators lack "gold standards" via driving scores or benchmarks. The composites derived in this analysis may be studied for further use where driving behavior standards are increasingly sought by clinicians and practitioners for a variety of populations, as well as by parents concerned about the readiness of their novice driving teen.