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1. Near crash characteristics among risky drivers using the SHRP2 naturalistic driving study

   https://doi.org/10.1016/j.jsr.2020.03.012  

   Seacrist, Thomas; Douglas, Ethan C.; Hannan, Chloe; Rogers, Rachel; Belwadi, Aditya; Loeb, Helen (April 2020, Journal of safety research)

   null (Ed.)

   Problem: Previous research have focused extensively on crashes, however near crashes provide additional data on driver errors leading to critical events as well as evasive maneuvers employed to avoid crashes. The Strategic Highway Research Program 2 (SHRP2) Naturalistic Driving Study contains extensive data on real world driving and offers a reliable methodology to study near crashes. The current study utilized the SHRP2 database to compare the rate and characteristics associated with near crashes among risky drivers. Methods: A subset from the SHRP2 database consisting of 4,818 near crashes for teen (16–19 yrs), young adult (20–24 yrs), adult (35–54 yrs), and older (70+ yrs) drivers was used. Near crashes were classified into seven incident types: rear-end, road departure, intersection, head-on, side-swipe, pedestrian/cyclist, and animal. Near crash rates, incident type, secondary tasks, and evasive maneuvers were compared across age groups. For rear-end near crashes, near crash severity, max deceleration, and time-to-collision at braking were compared across age. Results: Near crash rates significantly decreased with increasing age (p < 0.05). Young drivers exhibited greater rear-end (p < 0.05) and road departure (p < 0.05) near crashes compared to adult and older drivers. Intersection near crashes were the most common incident type among older drivers. Evasive maneuver type did not significantly vary across age groups. Near crashes exhibited a longer time-to-collision at braking (p < 0.01) compared to crashes. Summary: These data demonstrate increased total near crash rates among young drivers relative to adult and older drivers. Prevalence of specific near crash types also differed across age groups. Timely execution of evasive maneuvers was a distinguishing factor between crashes or near crashes. Practical Applications: These data can be used to develop more targeted driver training programs and help OEMs optimize ADAS to address the most common errors exhibited by risky drivers. 

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2. Age-Related Differences in Takeover Request Modality Preferences and Attention Allocation During Semi-autonomous Driving

   https://doi.org/10.1007/978-3-030-50252-2_11  

   Huang, G. (July 2020, International Conference on Human-Computer Interaction)

   Adults aged 65 years and older are the fastest growing age group worldwide. Future autonomous vehicles may help to support the mobility of older individuals; however, these cars will not be widely available for several decades and current semi-autonomous vehicles often require manual takeover in unusual driving conditions. In these situations, the vehicle issues a takeover request in any uni-, bi- or trimodal combination of visual, auditory, or tactile alerts to signify the need for manual intervention. However, to date, it is not clear whether age-related differences exist in the perceived ease of detecting these alerts. Also, the extent to which engagement in non-driving-related tasks affects this perception in younger and older drivers is not known. Therefore, the goal of this study was to examine the effects of age on the ease of perceiving takeover requests in different sensory channels and on attention allocation during conditional driving automation. Twenty-four younger and 24 older adults drove a simulated SAE Level 3 vehicle under three conditions: baseline, while performing a non-driving-related task, and while engaged in a driving-related task, and were asked to rate the ease of detecting uni-, bi- or trimodal combinations of visual, auditory, or tactile signals. Both age groups found the trimodal alert to be the easiest to detect. Also, older adults focused more on the road than the secondary task compared to younger drivers. Findings may inform the development of next-generation of autonomous vehicle systems to be safe for a wide range of age groups. 

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3. 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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4. Barriers to Preparing for Disasters: Age Differences and Caregiving Responsibilities

   https://doi.org/https://doi.org/10.1016/j.ijdrr.2021.102338  

   Cong, Z.; Chen, Z.; Liang, D. (July 2021, International journal of disaster risk reduction)

   null (Ed.)

   This study examined age differences in barriers to preparing for disasters and how caregiving responsibilities are associated with these barriers among different age groups. Using a sample of 1142 individuals from the 2017 Federal Emergency Management Agency National Household Survey, binary and multinomial logistic regressions were conducted to investigate the likelihood of encountering any or one of the two types of barriers, namely, barriers related to coping appraisal (i.e., capacity) and those related to threat appraisal (i.e., risk perception). Age was the key predictor and was categorized into five groups: 18–34, 35–49, 50–64, 65–74, and 75+. The results showed that the 18–34, 35–49, and 75+ age groups were more likely to have coping appraisal barriers than those aged between 65 and 74. In addition, being a caregiver increased the likelihood of having coping appraisal barriers. Interestingly, relative to the 65–74 age group, being a caregiver in the 18–34, 35–49, and 50–64 age groups would be more likely to have coping appraisal barriers. Our findings highlighted age patterns and heterogeneity among older adults. This study also directed attention to how disaster preparation behaviors were shaped by life course experiences. 

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5. The Effect of Driving Style on Responses to Unexpected Vehicle Cyberattacks

   https://doi.org/10.3390/safety9010005  

   Zhang, Fangda; Wang, Meng; Parker, Jah’inaya; Roberts, Shannon C (March 2023, Safety)

   Vehicle cybersecurity is a serious concern, as modern vehicles are vulnerable to cyberattacks. How drivers respond to situations induced by vehicle cyberattacks is safety critical. This paper sought to understand the effect of human drivers’ risky driving style on response behavior to unexpected vehicle cyberattacks. A driving simulator study was conducted wherein 32 participants experienced a series of simulated drives in which unexpected events caused by vehicle cyberattacks were presented. Participants’ response behavior was assessed by their change in velocity after the cybersecurity events occurred, their post-event acceleration, as well as time to first reaction. Risky driving style was portrayed by scores on the Driver Behavior Questionnaire (DBQ) and the Brief Sensation Seeking Scale (BSSS). Half of the participants also received training regarding vehicle cybersecurity before the experiment. Results suggest that when encountering certain cyberattack-induced unexpected events, whether one received training, driving scenario, participants’ gender, DBQ-Violation scores, together with their sensation seeking measured by disinhibition, had a significant impact on their response behavior. Although both the DBQ and sensation seeking have been constantly reported to be linked with risky and aberrant driving behavior, we found that drivers with higher sensation seeking tended to respond to unexpected driving situations induced by vehicle cyberattacks in a less risky and potentially safer manner. This study incorporates not only human factors into the safety research of vehicle cybersecurity, but also builds direct connections between drivers’ risky driving style, which may come from their inherent risk-taking tendency, to response behavior to vehicle cyberattacks. 

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