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As vehicles with SAE level 2 of autonomy become more widely deployed, they still rely on the human driver to monitor the driving task and take control during emergencies. It is therefore necessary to examine the Human Factors affecting a driver’s ability to recognize and execute a steering or pedal action in response to a dangerous situation when the autonomous system abruptly requests human intervention. This research used a driving simulator to introduce the concept of level 2 autonomy to a cohort of 60 drivers (male: 48%, female: 52%) of different age groups (teens 16 to 19: 32%, adults: 35 to 54: 37%, seniors 65+: 32%). Participants were surveyed for their perspectives on self-driving vehicles. They were then assessed on a driving simulator that mimicked SAE level 2 of autonomy. Participants’ interaction with the HMI was studied.

Autonomous and/or automated vehicles offer a host of future opportunities but leave many questions unanswered regarding their impact on crash avoidance or the ability of drivers to effectively scan and re-engage from self-driving mode when necessary to avoid crash scenarios. Considering a 16-year-old is several times more likely to die in an automobile crash than other licensed drivers, it was crucial to test both teenage drivers and adults to determine head-on collision avoidance abilities when subjected to a failing autopilot in a simulated autonomous vehicle. In this study, eight teenagers ages 16-19 and four experienced adults underwent four simulated drives (one manual practice drive and three simulated autonomous drives) using a hi-fidelity, Real Time Technologies SimDriver Simulator to represent being in a self-driving vehicle.

Driving simulators offer a safe alternative to on-road driving for the evaluation of performance. In addition, simulated drives allow for controlled manipulations of traffic situations producing a more consistent and objective assessment experience and outcome measure of crash risk. Yet, few simulator protocols have been validated for their ability to assess driving performance under conditions that result in actual collisions. This paper presents results from a new Simulated Driving Assessment (SDA), a 35- to-40-minute simulated assessment delivered on a Real-Time® simulator. The SDA was developed to represent typical scenarios in which teens crash, based on analyses from the National Motor Vehicle Crash Causation Survey (NMVCCS). A new metric, failure to brake, was calculated for the 7 potential rear-end scenarios included in the SDA and examined according two constructs: experience and skill.

Head injury is the most common cause of death and acquired disability in childhood. We seek to determine the influence of brain mechanical properties on inertial pediatric brain injury. Large deformation material properties of porcine pediatric and adult brain tissue were measured and represented by a first-order Ogden hyperelastic viscoelastic constitutive model. A 3-D finite element mesh was created of a mid-coronal slice of the brain and skull of a human adult and child (2 weeks old). Three finite element models were constructed: (1) a pediatric mesh with pediatric brain properties, (2) a pediatric mesh with adult tissue properties, and (3) an adult mesh with adult tissue properties. The skull was modeled as a rigid solid and an angular acceleration was applied in the coronal plane with center at C4/C5. The brain is assumed to be homogeneous and isotropic.

In vivo, tissue-level, mechanical thresholds for axonal injury in the guinea pig optic nerve were determined by comparing morphological injury to estimated in vivo tissue strain. The right optic nerve of adult male guinea pigs was stretched to one of seven ocular displacement levels. Morphological injury was detected three days post-stretch with neurofilament immunohistochemical staining (NF68). A companion set of in situ experiments was used to determine the empirical relationship between ocular displacement and optic nerve stretch. Logistics regression analysis, combined with sensitivity and specificity measures and receiver operating characteristic (ROC) curves were then used to predict strain thresholds for axonal injury. From this analysis, we determined three Lagrangian strain- based thresholds for morphological damage to the guinea pig white matter.

Little is known about the mechanism of pelvic injury in the pediatric population, an age range over which the pelvis undergoes tremendous structural change. We hypothesize that these structural changes influence pelvic fracture injury mechanisms. A probability sample of children under age 16 years in crashes were enrolled in an on-going crash surveillance system which links insurance claims data to telephone survey and crash investigation data. 15,725 children in side impact collisions were studied. Risk of pelvic fracture in side impact collisions was estimated and factors associated with these injuries were identified. Eight cases were examined using in-depth investigation to identify the injury mechanisms. Of our study sample, 0.10% of children suffered a pelvic fracture. The typical child with a pelvic fracture was a 12-15 year old female front row occupant of a passenger car involved in a struck side collision with intrusion.