Department Of Motor Vehicles Research Articles

Statistical Approaches Used in Studies Evaluating the Reliability of Autonomous Vehicles Based on Disengagements and Reaction Times

The rapid development of autonomous vehicle (AV) technology highlights the critical importance of enhancing the reliability of these vehicles. Due to the need to test the reliability of AVs, since 2014, the California Department of Motor Vehicles has permitted autonomous vehicle manufacturers to establish an AV Testing program, enabling them to test automated systems on the transportation network. With this, studies on the reliability of AVs have increased rapidly. The most emphasized issues regarding the reliability of AVs have been disengagements, accidents, and reaction times. In this study, disengagements and reaction times are categorized and explained in detail according to the data type, company, period, and statistical method. The data used in the studies cover the years 2014-2020. When examining studies on the reliability of AVs, until 2018, inferences were generally made using real data and descriptive statistics, particularly with methods such as correlation analysis and calculation of disengagements per mile, which investigates the relationship between distance traveled and disengagements. However, since 2018, machine learning has gained importance in evaluating AV reliability. It has been observed that regression, classification, and decision trees were frequently used during this period. Techniques such as deep transfer learning, text mining, and natural language processing also stand out. Furthermore, Software Reliability Growth Models are used to measure software reliability, playing an essential role in evaluating, analyzing, and improving the performance and reliability of AVs. This study aims to reveal the development and diversity of the statistical methods used to determine AV reliability. Additionally, this study aims to guide and provide insights to researchers in the field about the statistical approaches they can utilize.

Classification of autonomous vehicle crash severity: Solving the problems of imbalanced datasets and small sample size

Only a few researchers have shown how environmental factors and road features relate to Autonomous Vehicle (AV) crash severity levels, and none have focused on the data limitation problems, such as small sample sizes, imbalanced datasets, and high dimensional features. To address these problems, we analyzed an AV crash dataset (2019 to 2021) from the California Department of Motor Vehicles (CA DMV), which included 266 collision reports (51 of those causing injuries). We included external environmental variables by collecting various points of interest (POIs) and roadway features from Open Street Map (OSM) and Data San Francisco (SF). Random Over-Sampling Examples (ROSE) and the Synthetic Minority Over-Sampling Technique (SMOTE) methods were used to balance the dataset and increase the sample size. These two balancing methods were used to expand the dataset and solve the small sample size problem simultaneously. Mutual information, random forest, and XGboost were utilized to address the high dimensional feature and the selection problem caused by including a variety of types of POIs as predictive variables. Because existing studies do not use consistent procedures, we compared the effectiveness of using the feature-selection preprocessing method as the first process to employing the data-balance technique as the first process.Our results showed that AV crash severity levels are related to vehicle manufacturers, vehicle damage level, collision type, vehicle movement, the parties involved in the crash, speed limit, and some types of POIs (areas near transportation, entertainment venues, public places, schools, and medical facilities). Both resampling methods and three data preprocessing methods improved model performance, and the model that used SMOTE and data-balancing first was the best. The results suggest that over-sampling and the feature selection method can improve model prediction performance and define new factors related to AV crash severity levels.

State Department of Motor Vehicles Reporting Mandates of Dementia Diagnoses and Dementia Underdiagnosis

With older drivers representing the fastest growing segment of the driver population and dementia prevalence increasing with age, policymakers face the challenge of balancing road safety and mobility of older adults. In states that require reporting a dementia diagnosis to the Department of Motor Vehicles (DMV), individuals with dementia may be reluctant to disclose symptoms of cognitive decline, and clinicians may be reluctant to probe for those symptoms, which may be associated with missed or delayed diagnoses. To assess whether DMV reporting policies for drivers with dementia are associated with primary care clinicians' underdiagnosing dementia. This cross-sectional study used data from the 100% Medicare fee-for-service program and the Medicare Advantage plans from 2017 to 2019 on 223 036 primary care clinicians with at least 25 Medicare patients. Statistical analysis was performed from July to October 2023. State DMV reporting policies for drivers with dementia. The main outcome was a binary variable indicating whether the clinician underdiagnosed dementia or not. Each clinician's expected number of dementia cases was estimated using a predictive model based on patient characteristics. Comparing the estimation with observed dementia diagnoses identified clinicians who underdiagnosed dementia vs those who did not, after accounting for sampling errors. Four states have clinician reporting mandates, 14 have mandates requiring drivers to self-report dementia diagnoses, and 32 states and the District of Columbia do not have explicit requirements. Among primary care clinicians in states with clinician reporting mandates (n = 35 620), 51.4% were female, 91.9% worked in a metropolitan area, and 19.9% of the patient panel were beneficiaries dually eligible for Medicare and Medicaid. Among primary care clinicians in states with patient self-reporting mandates (n = 57 548), 55.7% were female, 83.1% worked in a metropolitan area, and 15.4% of the patient panel were dually eligible for Medicare and Medicaid. Among clinicians in states without mandates, 55.7% were female, 83.0% worked in a metropolitan area, and 14.6% of the patient panel were dually eligible for Medicare and Medicaid. Clinicians in states with clinician reporting mandates had an adjusted 12.4% (95% CI, 10.5%-14.2%) probability of underdiagnosing dementia compared with 7.8% (95% CI, 6.9%-8.7%) in states with self-reporting and 7.7% (95% CI, 6.9%-8.4%) in states with no mandates, an approximately 4-percentage point difference (P < .001). Results of this cross-sectional study of primary care clinicians suggest that mandatory DMV policies for clinicians to report patients with dementia may be associated with a higher risk of missed or delayed dementia diagnoses. Future research is needed to better understand the unintended consequences and the risk-benefit tradeoffs of these policies.

Investigating the impacts of autonomous vehicles on crash severity and traffic safety

Traffic accidents are a nationwide public health concern, but autonomous vehicles (AVs) have the potential to significantly reduce accident severity and frequency by eliminating their most common cause, human error. By analyzing the data published by California’s Department of Motor Vehicles, researchers have identified the factors that influence AV crash severity, however, none do so through a literature review. This paper’s aims are multi-faceted: to understand AVs’ operation on public roadways by identifying and classifying the factors contributing to accident severity, to develop a list of strategies that address the public’s safety concerns, and to acknowledge the ethics of unavoidable collisions. To fulfill these objectives, a comprehensive literature review was conducted based on a keyword search. Following a multi-step screening and exclusion process, detailed review was performed of 107 relevant publications, and the factors contributing to increased crash severity were classified into 14 categories. The literature revealed that AVs are not at fault in most accidents, although they have a higher propensity than conventional cars to be involved in rear-end collisions, and they are specifically designed to minimize the number of accidents, but may face unavoidable ones. For the benefit of policymakers and manufacturers, 11 strategies that address the moral dilemma of these accidents and 7 strategies that address concerns about AV safety to improve public perception were identified.

Understanding key factors in automated vehicle collisions: Automating data extraction and analyzing key insights using explainable AI

With the growing development and deployment of automated vehicles (AVs), it is crucial to understand the associated risks and factors contributing to collisions involving AVs. California possesses an immense amount of publicly available data from AV testing due to the requirement laid out by the California Department of Motor Vehicles (DMV), which requires all automated vehicle operators to report collisions for any level of severity. However, this information is reported in specific forms and requires a laborious task to aggregate data from these reports. This study creates an automated data extraction system for these reports and analyses collision characteristics using logistic regression models as well as XGBoost models with SHapley Additive exPlanation (SHAP) interpretation. Additionally, these characteristics are matched with the characteristics of non-automated vehicles (non-AVs) for the same region. The results of the study indicate that rear-end collisions are the most common type of collision observed in currently deployed AVs. The analysis further revealed an increased likelihood of injury-prone rear-end collisions in AVs at intersections compared to non-AVs. Transportation policymakers and researchers should take these safety concerns into account when addressing AV deployment and developing appropriate measures to mitigate collision risks in mixed fleet conditions.

Reporting Requirements, Confidentiality, and Legal Immunity for Physicians Who Report Medically Impaired Drivers

Physicians play an important role in assessing patients' ability to drive. There is a dearth of peer-reviewed information on policies regarding physician reporting of medically impaired drivers. To investigate state reporting requirements and the availability of confidentiality and legal immunity for physicians who report medically impaired drivers. This cross-sectional study was conducted from November 1 to 30, 2022, in 3 rounds. First, all 50 US states' Department of Motor Vehicle (DMV) websites were systematically reviewed. Second, DMV staff from each state were surveyed via telephone. Third, each state's legal codes for driver licensing were reviewed. Outcome measures included the percentage of states with mandatory and voluntary reporting policies, reporting instructions on DMV websites, anonymous reporting options, and legal immunity for reporting physicians, in addition to characteristics of states' mandatory reporting policies (ie, types of medical conditions that require reporting) and policies surrounding the confidentiality of reports. The data were analyzed using descriptive statistics. One-third of state DMV websites (17 [34%]) lacked instructions regarding physician reporting. Six states had mandatory reporting requirements; 4 of these states only required reporting of conditions characterized by lapses of consciousness. Only 3 states (6%) accepted anonymous reports, and 7 states (14%) deemed physician reports of medically impaired drivers confidential without exception. Nearly one-third of states (15 [30%]) deemed reports by physicians confidential, with the exception that reported drivers could find out who reported them if they asked for a copy of the reporting form. Most states (37 [74%]) had statutes that protected physicians from liability related to reporting medically impaired drivers. This cross-sectional study of state reporting requirements regarding medically impaired drivers found many differences in state policies regarding mandatory reporting and the conditions that require reporting. There was also limited availability of online reporting instructions, anonymous reporting options, and legal protections for reporting physicians.

Investigating the impact of influential factors on crash types for autonomous vehicles at intersections

Autonomous Vehicles (AVs) are being promoted as an emerging technology with the potential to improve traffic efficiency and safety. However, the scarcity of publicly available AV crash data contributes to a limited understanding of safety issues for deploying AVs. To gain a deeper understanding of the factors contributing to AV crashes, this study created a unique dataset by combining 445 intersection AV crashes from the California Department of Motor Vehicles with detailed roadway geometric and traffic characteristic data. To address the issue of imbalanced data, the Borderline synthetic minority oversampling technique (BL-SMOTE) was utilized in this study. A framework for analyzing AV crash types at intersections was also developed using the Categorical gradient boosting (Catboost) and Bayesian networks (BNs) models. The results show that inappropriate vehicle maneuvers, such as sudden lane changes or speed control, can increase the likelihood of an AV being involved in a sideswipe crash. Additionally, in scenarios where the AADT of the major road is lower, AVs are more prone to rear-end crashes when proceeding. These findings can provide valuable insights for creating safety management strategies in different situations and improve the development of innovative car warning technologies, such as rear-end collision warnings.

Authority Transfer According to a Driver Intervention Intention Considering Coexistence of Communication Delay

Recently, interest and research on autonomous driving technology have been actively conducted. However, proving the safety of autonomous vehicles and commercializing autonomous vehicles remain key challenges. According to a report released by the California Department of Motor Vehicles on self-driving, it is still hard to say that self-driving technology is highly reliable. Until fully autonomous driving is realized, authority transfer to humans is necessary to ensure the safety of autonomous driving. Several technologies, such as teleoperation and haptic-based approaches, are being developed based on human-machine interaction systems. This study deals with teleoperation and presents a way to switch control from autonomous vehicles to remote drivers. However, there are many studies on how to do teleoperation, but not many studies deal with communication delays that occur when switching control. Communication delays inevitably occur when switching control, and potential risks and accidents of the magnitude of the delay cannot be ignored. This study examines compensation for communication latency during remote control attempts and checks the acceptable level of latency for enabling remote operations. In addition, supplemented the safety and reliability of autonomous vehicles through research that reduces the size of communication delays when attempting teleoperation. It is expected to prevent human and material damage in the actual accident situation.

1 Benefits of Speed of Processing Training in Older Adults

Objective:Physical, sensory, and cognitive function are all related to successful aging, quality of life, and independence in older age. Decline in cognitive function, in particular, can create difficulty in many Instrumental Activities of Daily Living such as driving and other aspects of everyday function. Loss of driving competence can subsequently lead to depression, isolation, loss of independence and reduced quality of life. Results from a large randomized controlled trial investigating the long-term impact of Speed of Processing Training will be presented demonstrating the impact of such training on cognitive and everyday function. Speed of Processing Training is an adaptive cognitive intervention administered on a computer that improves visual attention, and has been shown to enhance the connections needed for visual attention using task-driven and resting state fMRI.Participants and Methods:The ACTIVE clinical trial recruited a volunteer sample of 2,832 community dwelling older adults between the ages of 65 and 94 years at six different field sites in six different states. This study evaluated the impact of three cognitive interventions targeted at improving cognitive and everyday function which served as contract control conditions for each other. Participants were randomly assigned to speed of processing training, reasoning training, memory training, or a no-contact control group. Follow-up testing was collected in person for ten years, and at twenty years final archival data was collected from the Departments of Motor Vehicles at each of the six field sites, as well as the Medicare/Medicaid data across the twenty years since enrollment. Outcomes included measures of cognitive function, measures of IADL (including crash involvement and driving cessation), and secondary outcomes of mobility (falls, driving habits, life space), quality of life, mortality and health conditions (from Medicare Records).Results:Results will be presented with respect to everyday function including several longitudinal measures of mobility: number of miles driven per year and driving difficulty, rates of driving cessation, and crash involvement. Random effects modelling, cox proportional hazards, and rate ratios will be presented illustrating the positive impact of cognitive training on these outcome measures. In particular, Speed of Processing Training was found to sustain driving competence with respect to continued driving relative to other types of training, and to reduce the risk of crash involvement over ten years. Furthermore, decline in Speed of Processing was the only cognitive measure predictive of crash involvement longitudinally in the no-contact control group. Positive benefits were also observed for Health Related Quality of Life (HRQoL), and depression.Conclusions:The ACTIVE clinical trial, among other studies, has demonstrated that cognitive training can have long-term positive benefit on the everyday abilities, quality of life, and continued independence of older adults.

A Public Organization Provided a Poor Service: Is There Anything They Can Do to Make It Right with the Citizen?

This article aimed to investigate the effectiveness of service recovery strategies in a public organization. Justice theory and cognitive appraisal theory were used to develop hypotheses that predicted the impact of recovery strategies (i.e., the quality of the reperformed service, empathetic apology, and the combined effects of empathetic apology and quality of the reperformed service) on justice perceptions and emotional responses. The notion is that these recovery strategies will compensate citizens for the loss incurred during the service failure, thus increasing their perceptions of justice and emotional responses. No article was found to examine the combined effects of empathetic apology and both high and low reperformance on justice and emotional perceptions. To conduct the study, an online survey experiment consisting of 6 vignettes was administered to 1,000 individuals who were recruited by Qualtrics. Furthermore, the organization in the vignettes where the service failure and recovery occurred was the Department of Motor Vehicles. The results from the analyses supported most of the hypotheses. Reperformed service was generally most beneficial when it was done at a high level. Next, combining empathetic apology and high reperformed service recovery was largely found to be more effective than just employing one service recovery strategy or combining empathetic apology and low reperformance. Finally, performing an empathetic apology and a low reperformance is generally not more effective than employing only one strategy. The implication of these results is thoroughly discussed in the article.

Randomized intervention to assess the effectiveness of an educational video on organ donation intent among Hispanics in the New York metropolitan area.

The Hispanic community has a high demand for organ donation but a shortage of donors. Studies investigating factors that could promote or hinder organ donation have examined emotional video interventions. Factors acting as barriers to organ donation registration have been classified as: (1) Bodily integrity; (2) medical mistrust; (3) "ick"-feelings of disgust towards organ donation; and (4) "jinx"-fear that registration may result in one dying due to premeditated plans. We predict that by providing necessary information and education about the donation process via a short video, individuals will be more willing to register as organ donors. To determine perceptions and attitudes regarding barriers and facilitators to organ donation intention among Hispanic residents in the New York metro politan area. This study was approved by the Institutional Review Board at Northwell Health. The approval reference number is No. 19-0009 (as presented in Supplementary material). Eligible participants included Hispanic New York City (NYC) residents, 18 years of age and above, who were recruited voluntarily through Cloud Research and participated in a larger randomized survey study of NYC residents. The survey an 85-item Redcap survey measured participant demographics, attitudes, and knowledge of organ donation as well as the intention to register as an organ donor. Attention checks were implemented throughout the survey, and responses were excluded for those who did fail. Participants were randomly assigned two-between subject conditions: To view a short video on organ donation and then proceed to complete the survey (i.e., video first) and view the same video at the end of the survey (video last). No intra-group activities were conducted. This study utilized an evidenced-based emotive educational intervention (video) which was previously utilized and was shown to increase organ donation registration rates at the Ohio Department of Motor Vehicles. Results were analyzed using Jamovi statistical software. Three hundred sixty-five Hispanic individuals were included in the analysis. Once consent was obtained and participants entered the survey (the survey sample is presented in Supplementary material), participants were asked to report on demographic variables and their general impression of organ donation after death. The video depicted stories regarding organ donation after death from various viewpoints, including from the loved ones of a deceased person who died waiting for a transplant; from the loved ones of a deceased person whose organs were donated upon death; and, from those who were currently waiting for a transplant. Using a binomial logistic regression, the analysis provides information about the relationship between the effects of an emotive video and the intention to donate among Hispanic participants who were not already registered as donors. The willingness to go back and register was found to be significantly more probable for those who watched the emotive video before being asked about their organ donation opinions (odds ratio: 2.05, 95% confidence interval: 1.06-3.97). Motivations for participation in organ donation were also captured with many stating the importance of messages coming from "people like me" and a message that highlights "the welfare of those in need". Overall, the findings suggest that using an emotive video that addresses organ donation barriers to prompt organ donation intentions can be effective among the Hispanic populous. Future studies should explore using targeted messaging that resonates with specific cultural groups, highlighting the welfare of others. This study suggests that an emotive educational intervention is likely to be effective in improving organ donation registration intent among the Hispanic population residing in NYC.

Exploring Customers’ Feedback on the Technological and Operational Changes at the Departments of Motor Vehicles Due to Covid-19 Pandemic

In Early 2020, after COVID-19 was declared a pandemic, the Center for Disease Control and Prevention (CDC) provided guidelines to combat the spread of COVID-19. As a result, Departments of Motor Vehicles (DMVs) introduced several technological changes and modified operation protocols. This study explored the changes of customer feedback resulting from the changes in DMVs operations across the United States. A total of 24[Formula: see text]164 Google reviews were collected from 69 cities in 43 States. Sentiment analysis, text network, and logistic regression were applied to evaluate the changes of the feedback before and after the lockdown period. It was found that the post-lockdown reviews had higher positive polarity and low negative polarity than pre-lockdown reviews. Furthermore, positive feedback topics, such as excellent service and short waiting time, increased significantly during the post-lockdown era. Keywords that showed a higher likelihood of higher ratings include appointment, wait time, and minutes. Conversely, lower ratings were associated with keywords — customer services, hours, and people, among others. Further, the logistic regression showed that the odds of high ratings increased nearly three times when COVID-19-related keywords were mentioned. Several recommendations to improve operations are presented in this study. It is expected that the findings will benefit DMV operators and policymakers across the United States.

Let's come to order: The influence of question order on willingness to register as an organ donor

RationaleAs over 90% of people who register to be organ donors do so at the Department of Motor Vehicles (DMV), DMVs are perceived as a key context for increasing donor registration rates. Scholars have recently noted that the driver's license application itself, including the placement of the donor registration item relative to other questions, can possibly influence donor registration behavior. The goal of the current study was to experimentally investigate this possibility. MethodWe conducted an experiment using Amazon's Mechanical Turk (MTurk) between March and May of 2021 to investigate the influence of question order on donor registration willingness. Participants received a question regarding their willingness to register either before or after a series of health and legal questions often asked at DMVs. ResultsThe placement of the donor registration question had a positive effect on registration willingness for non-registered individuals (OR = 2.01, 95% CI [1.59, 2.54]) and previously registered donors (OR = 2.57, 95% CI [2.22, 2.99]). ConclusionChanging the question order of driver's license applications has the potential to influence registration rates.

Happy birthday: you get to vote!

AbstractThis paper estimates the effect of automatic voter registration (AVR) on voter turnout in California and Oregon. AVR systems register to vote all eligible individuals who transact with proscribed government agencies, most commonly the Department of Motor Vehicles (DMVs). The article isolates one part of the causal impact of AVR on turnout by taking advantage of a temporal feature of license renewals. Many individuals interact with the DMV periodically due to the need to renew drivers' licenses. Because licenses in both California and Oregon expire on birthdays, an individual's birth date can be treated as an exogenous variable discriminating between some individuals who are registered to vote in time for the election, while others are not. Our instrumental variable analysis compares registration and voting rates for individuals with birth dates prior and subsequent to the voter registration deadline. After calculating a causal effect of AVR on turnout at the individual level, we extrapolate this AVR “birthday” effect to overall voter turnout for these states.

Risk Assessment and Enhancement Suggestions for Automated Driving Systems through Examining Testing Collision and Disengagement Reports

The California Department of Motor Vehicles (DMV) reports, including disengagement and collision reports, provide information on each accident or disengagement activity for on-road testing of autonomous driving systems (ADSs) and autonomous vehicles (AVs). Unfortunately, current DMV reports have been misleading in relation to many key details, making it challenging for readers of those reports to discern the events’ root causes and interrelationships. Therefore, appropriate systematic classification methods and principles need to be adopted. We follow an identification method similar to fault tree analysis (FTA) with the help of the driving reliability and error analysis method (DREAM 3.0) and the Haddon matrix to find the potential key accident factors from all disengagement data. We also conduct ADS risk assessments of potential disengagements and genuine accidents classified by traditional accident types. In addition, the automated driving system is composed of various software modules, and a classification method that is suitable from the standpoint of ADS software developers is developed in this paper. Next, we sort out the characteristics of the most frequent accidents based on the risk assessment results. Finally, we propose a workable risk reduction solution according to the characteristics of accidents.

Autonomous vehicles and traffic accidents

Traffic accidents are a national public health crisis that result in multiple injuries and fatalities. Conventional vehicle crashes are largely attributed to human errors, but autonomous vehicles (AVs) have the ability to minimize human involvement and improve road safety. To assure the safe operation of these vehicles, extensive field and simulation experiments are being conducted, and this study delves into their impact on traffic accidents and injuries by providing a thorough review of the relevant literature on this topic, as well as an evaluation of the California Department of Motor Vehicles (CA DMV) crash data to understand how AVs function on public roadways. The moral ramifications of AVs and unavoidable collisions are also addressed. The results of this research revealed that AVs are more likely to be involved in rear-end collisions but are not the faulty party in most accidents, which supports the premise that they improve road safety. Although they are designed to reduce accidents, AVs may nevertheless get into unavoidable collisions. Manufacturers could try to program the vehicles with shared ethical standards to handle this issue, or they could develop a flexible computational strategy that considers a wide range of ethical standards. Transportation experts will gain a thorough understanding of AV collisions and interactions in a mixed environment through this review, and AV manufacturers and legislators will gain greater insight into the moral and legal ramifications of inevitable crashes.

Comparison of Chronic Health Conditions and Health Behaviors in Long-Term Young Adult Hodgkin Lymphoma (YAHL) Survivors to That of Their Unaffected Co-Twin Controls

Background: Long-term survivors of young adult Hodgkin lymphoma (YAHL) are at risk of experiencing late effects due to cancer diagnosis and treatment. Many studies have compared survivors to unrelated controls from various sources, which could introduce confounding. Unaffected co-twins of cases are an ideal comparison because they are matched on many exposures, extending to in utero, are the same age and cohort, and are partially or completely matched on genome. Here we compare medical conditions, life milestones, lifestyle and behaviors, and psychological well-being in 35 long-term YAHL survivors and their unaffected co-twin controls(70 individual twins, 35 twin pairs) to obtain a more valid risk estimate of the contribution of the cancer and its treatment. Methods: Participants were enrolled in either the International Twin Study (national volunteer twin registry) or California Twin Program (population-based twin registry based on California birth certificates linked to the Department of Motor Vehicles) and completed online questionnaires including chronic health conditions, health behaviors, the achievement of developmental milestones (e.g., college degree attainment, employment, and having children), and life satisfaction. Twin comparisons were made using conditional logistical regression with p-values estimated using McNemar's test. Adjustment was not necessary as twins were mostly matched on potential confounders. All analyses were performed in STATA, version 17.0. Results: The mean age at diagnosis among survivors was 27 years (median: 26, range 16-48 years). The mean age at participation of survivors and unaffected co-twins was 56 years of age (median: 55, range: 40-74 years), with an average interval of 29 years between diagnosis and participation. 57% of YAHL survivors and 6% co-twin controls were female. 89% of participants were non-Hispanic white. 54% were monozygotic (MZ); 32% were like-sex dizygotic (DZ) and 14% were unlike-sex DZ. We observed significant increases in chronic medical conditions among YAHL survivors who were 4.33 times as likely to report a secondary malignancy as their unaffected co-twin controls (p=022). Breast cancer was the most commonly reported cancer, occurring in 8 survivors and one co-twin control (odds ratio [OR]=8.0, p= 0.05); all survivors with breast cancer had radiation therapy. Thyroid disease occurred in 23 survivors (18 had hypothyroidism) and 5 co-twin controls (OR=¥, p <0.001). Cardiovascular disease (any) occurred in 16 survivors and 5 co-twin controls (OR=12.0, p=0.017). Six survivors and no co-twin controls were affected with lung disease, mainly pulmonary fibrosis (p=0.031). After 29 years post-diagnosis, there were no significant differences between YAHL survivors and their twins with respect to healthcare utilization, substance use, physical activity, or developmental milestones relative to their unaffected co-twin. In particular, both twins had similar numbers of children and educational attainment. Life satisfaction was significantly lower among YAHL survivors (OR=¥, p=.043) but was unrelated to the occurrence of chronic conditions (p = 0.10). Conclusions: Decades after diagnosis, excess chronic conditions and lower life satisfaction were observed among YAHL survivors compared to their unaffected co-twin. The risk of cancer and chronic disease attributable to YAHL and its treatment is a more valid estimate when compared to unaffected co-twin controls of the survivors, who may also develop chronic conditions due to other factors in common. There were no differences in engagement in health behaviors or achievement of developmental milestones, potentially due to the importance of shared early-life experiences on these characteristics.

A comparative study of collision types between automated and conventional vehicles using Bayesian probabilistic inferences

Introduction: Automated vehicle (AV) technology is a promising technology for improving the efficiency of traffic operations and reducing emissions. This technology has the potential to eliminate human error and significantly improve highway safety. However, little is known about AV safety issues due to limited crash data and relatively fewer AVs on the roadways. This study provides a comparative analysis between AVs and conventional vehicles on the factors leading to different types of collisions. Method: A Bayesian Network (BN) fitted using the Markov Chain Monte Carlo (MCMC) was used to achieve the study objective. Four years (2017–2020) of AV and conventional vehicle crash data on California roads were used. The AV crash dataset was acquired from the California Department of Motor Vehicles, while conventional vehicle crashes were obtained from the Transportation Injury Mapping System database. A buffer of 50 feet was used to associate each AV crash and conventional vehicle crash; a total of 127 AV crashes and 865 conventional vehicle crashes were used for analysis. Results: Our comparative analysis of the associated features suggests that AVs are 43% more likely to be involved in rear-end crashes. Further, AVs are 16% and 27% less likely to be involved in sideswipe/broadside and other types of collisions (head-on, hitting an object, etc.), respectively, when compared to conventional vehicles. The variables associated with the increased likelihood of rear-end collisions for AVs include signalized intersections and lanes with less than 45 mph speed limit. Conclusions: Although AVs are found to improve safety on the road in most types of collisions by limiting human error leading to vehicle crashes, the current state of the technology shows that safety aspects still need improvement.

Disengagement Cause-and-Effect Relationships Extraction Using an NLP Pipeline

The advancement in machine learning and artificial intelligence promotes the testing and deployment of autonomous vehicles (AVs) on public roads. The California Department of Motor Vehicles (CA DMV) has launched the Autonomous Vehicle Tester Program, which collects and releases reports related to Autonomous Vehicle Disengagement (AVD) from autonomous driving. Understanding the causes of AVD is critical to improving the AV system’s safety and stability and providing guidance for AV testing and deployment. In this work, we built a scalable end-to-end pipeline to collect, process, model, and analyze the disengagement reports released from 2014 to 2020 using natural language processing and deep transfer learning. The analysis of disengagement data using taxonomy, visualization, and statistical tests revealed the trends of AV testing, cause frequency, and significant relationships between causes and effects of AVD. We found that (1) manufacturers tested AVs intensively during the Spring and/or Winter, (2) test drivers initiated more than 80% of the disengagement while more than 75% of the disengagement were because of errors in perception, localization & mapping, planning and control of the AV system, and (3) there was a significant relationship between the initiator of AVD and the cause category. This study serves as a successful practice of deep transfer learning using pre-trained models and generates a consolidated disengagement database allowing further investigation for other researchers. The related code and data are available on github. <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</xref>

Assessing the Effectiveness of a Primary Care Provider Office-Based Intervention for Increasing Organ Donor Registration Rates in Two New York State Counties.

Introduction: Despite strong public support, organ donor registration rates (RR) continue to lag while need only grows. In the United States, the traditional registration site is the Department of Motor Vehicles (DMV), however Primary care provider (PCP) offices have been considered as alternate locations for increasing RR. Methods: Twelve PCP offices across 2 New York Counties were subjected to a control week where participants received only a registration opportunity and an intervention week with the addition of a motivational poster and informational brochure. Zip code level sociodemographic data were obtained for each site. RR from the DMV over the same period served as historical control. Results: There were 1292 participants in the control phase and 1099 in the experimental phase. New registration rate for the control was 33.8% (289/897); experimental phase 7.88% (61/769); DMV registration 21.02% (1902/9050). The intervention was associated with a significant decrease in registrations (OR 0.181 (95% CI 0.135-0.244, P < 0.001)). Offices were clustered based on sociodemographic factors and regressed in 2 clusters. Lower educational attainment was associated with lower registration in the first but not second cluster (OR = 0.948 (0.923-0.974, P < 0.001)). Conclusions: This study provided evidence that PCP offices were a feasible site for organ donor registration and calls into question the efficacy of written materials-only interventions for increasing organ donor RR. It reiterated the negative effect of lower educational attainment on registration and suggested future studies focus on more active methods of engagement.