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1. Crash rates over time among younger and older drivers in the SHRP 2 naturalistic driving study

   https://doi.org/2020.03.001  

   Bruce G. Simons-Morton, Pnina Gershon ( June 2020 , Journal of safety research)

   null (Ed.)

   To examine crash rates over time among 16–17-year-old drivers compared to older drivers. Methods: Data were from a random sample of 854 of the 3,500 study participants in SHRP 2, a U.S.national, naturalistic driving (instrumented vehicle) study. Crashes/10,000 miles by driver age group, 3-month period, and sex were examined within generalized linear mixed models. Results: Analyses of individual differences between age cohorts indicated higher incidence rates in the 16–17-year old cohort relative to older age groups each of the first four quarters (except the first quarter compared to 18 – 20 year old drivers) with incident rate ratios (IRR) ranging from 1.98 to 18.90, and for the full study period compared with drivers 18–20 (IRR = 1.69, CI = 1.00, 2.86), 21 to 25 (IRR = 2.27, CI = 1.31, 3.91), and 35 to 55 (IRR = 4.00, CI = 2.28, 7.03). Within the 16–17-year old cohort no differences were found in rates among males and females and the decline in rates over the 24-month study period was not significant. Conclusions: The prolonged period of elevated crash rates suggests the need to enhance novice young driver prevention approaches such as Graduated Driver’s Licensing limits, parent restrictions, and postlicensure supervision and monitoring. Practical Applications: Increases are needed in Graduated Driver’s Licensing limits, parent restrictions, and postlicensure supervision and monitoring. 

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2. Investigating Traffic Crashes Involving Autonomous Vehicles

   Torres, Jesus ( May 2021 , IISE Annual Conference Proceedings)

   A. Ghate, K. Krishnaiyer (Ed.)

   Deaths due to road traffic accidents are one of the leading causes of death in the United States. Furthermore, the economic impact of road traffic accidents accounts for about 3% of the United States' annual gross domestic product (GDP). In the past decade, extensive research has focused on autonomous vehicles (AVs). This technology is said to help prevent traffic accidents while promoting road traffic safety. This study aims to investigate the safety performance of AVs and identify the significant risk factors associated with the AV collisions. The study considers more than 200 crashes involving AVs and includes vehicle factors, environmental factors, collision type and crash severity. Multinomial logistic regression was conducted with collision type. The results showed no statistically significant risk factors to crash severity. However, movement preceding to collision contributes significantly to collision type. When both vehicles are moving, there's a higher likelihood of an angled collision, 47% to be exact. The other scenario which demonstrates a high probability of an angled collision is when the AV vehicle is not moving while the other is moving. The highest probability for a rear-end collision to occur is when the AV vehicle is not moving while the other is moving. This scenario makes up 55% of the entire rear-end collisions. As for the second-highest proportion, both vehicles moving, it consists of 42%. The research shall help reduce AV involved collisions and increase driving safety. 

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3. Legal Framework for Rear-End Crashes in Mixed-Traffic Platooning: A Matrix Game Approach

   https://doi.org/10.3390/futuretransp3020025  

   Chen, Xu ; Di, Xuan ( June 2023 , Future Transportation)

   Autonomous vehicles (AV) hold great potential to increase road safety, reduce traffic congestion, and improve mobility systems. However, the deployment of AVs introduces new liability challenges when they are involved in car accidents. A new legal framework should be developed to tackle such a challenge. This paper proposes a legal framework, incorporating liability rules to rear-end crashes in mixed-traffic platoons with AVs and human-propelled vehicles (HV). We leverage a matrix game approach to understand interactions among players whose utility captures crash loss for drivers according to liability rules. We investigate how liability rules may impact the game equilibrium between vehicles and whether human drivers’ moral hazards arise if liability is not designed properly. We find that compared to the no-fault liability rule, contributory and comparative rules make road users have incentives to execute a smaller reaction time to improve road safety. There exists moral hazards for human drivers when risk-averse AV players are in the car platoon.

    

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4. Learner Driver Experience and Teenagers’ Crash Risk During the First Year of Independent Driving

   https://doi.org/10.1001/jamapediatrics.2020.0208  

   Ehsani, Johnathon P. ; Gershon, Pnina ; Grant, Brydon J. ; Zhu, Chunming ; Klauer, Sheila G. ; Dingus, Tom A. ; Simons-Morton, Bruce G. ( April 2020 , JAMA pediatrics)

   null (Ed.)

   IMPORTANCE One mechanism for teenagers’ elevated crash risk during independent driving may be inadequate learner driving experience. OBJECTIVE To determine how learner driver experience was associated with crash risk during the first year of independent driving. DESIGN, SETTING, AND PARTICIPANTS Youth aged 15.5 to 16.1 years at recruitmentwere eligible to participate. Participants’ vehicles were instrumented with sensors, and driving was recorded during the learner period through 1 year of independent driving. Data were collected from January 2011 through August 2014 in southwestern Virginia. EXPOSURES The amount, consistency and variety of practice, driving errors, and kinematic risky driving (KRD) rates during the learner period were recorded. Surveys, including one on sensation-seeking personality traits, were assessed at baseline. MAIN OUTCOMES AND MEASURES Cox proportional hazard regressions examined associations between individual characteristics and learner driving experience with driving time to first crash and all crashes in the first year of independent driving. So that hazard ratios (HRs) can be directly comparable, units of measurement were standardized to the interquartile range. RESULTS Of 298 individuals who responded to recruitment, 90 fulfilled the criteria and 82 completed the study (of whom 75 were white [91%] and 44 were girls [54%]). Teenage participants drove a mean (SD) of 1259.2 (939.7) miles over 89 days during the learner period. There were 49 property-damage crashes and/or police-reportable crashes during independent driving. Factors associated with driving time to first crash included higher sensation-seeking personality scale scores (HR, 1.67 [95%CI, 1.08-2.57] per 0.75-unit increase), learner driving KRD rates (HR, 1.27 [95%CI, 1.12-1.43] per 9.24-unit increase), and learner driving errors (HR, 0.44 [95%CI, 0.22-0.86] per increase of 6.48 errors). Similar results were obtained for all crashes in the first year, with the addition of consistency of learner driving practice (HR, 0.61 [95%CI, 0.38-0.99] per 0.23-unit increase). CONCLUSIONS AND RELEVANCE Individual characteristics and learner driving experiences were associated with crash risk during independent driving. As expected, there was an association between sensation seeking and crashes. Elevated KRD rates during the learner period may reflect risky driving behavior among novices or tolerance to abrupt maneuvers by parents who supervise driving. Consistent practice throughout the learner period could reduce teenage crash risk, which is supported by learning theories indicating distributed practice is effective for developing expertise. Errors during practice may constitute learning events that reinforce safer driving. Physicians could encourage parents to provide opportunities for regular practice driving and monitor their teenager’s KRD rates during the learner period using in-vehicle or smartphone-based technology. 

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5. Identifying Important Risk Factors Associated with Vehicle Injuries using Driving Behavior Data and Predictive Analytics

   M. Monselise, O. S. ( January 2019 , IEEE ICHI)

   Road injuries are rated among the top 10 causes of death by the World Health Organization, and the only one that is not a disease. The total economic cost of motor vehicle crashes in the United States was estimated to be $242 billion a year. This study examines multiple factors of accidents simultaneously with a goal of generating an interpretable model that can predict the occurrence of an accident given road conditions and driver behavior. The study compared 4 machine learning and deep learning modeling techniques on a dataset of 7707 trips collected by the Second Strategic Highway Research Program. A gradient boosted model was found to be most accurate and interpretable in accident prediction. This modeling technique also allows us to rank the feature importance of the factors in the model. The study finds that driver behavior, pre-incident maneuvers and secondary task duration are the most important variables in the predictive model. Using these conclusions will allow us to perform more work to infer these accident causes directly from vehicle sensor data in the future. 

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