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Inadequate situation awareness and response are increasingly recognized as prevalent critical errors that lead to young driver crashes. To identify and assess key indicators of young driver performance (including situation awareness), we previously developed and validated a Simulated Driving Assessment (SDA) in which drivers are safely and reproducibly exposed to a set of common and potentially serious crash scenarios. Many of the standardized safety measures can be calculated in near real-time from simulator variables. Assessment of situation awareness, however, largely relies on time-consuming data reduction and video coding. Therefore, the objective of this research was to develop a near real-time automated method for analyzing general direction and location of driver's gaze in order to assess situation awareness.

Computational modeling is a potentially powerful tool to provide information about the mechanisms of traumatic brain injury. In order to ensure that the estimates calculated by these computer models provide the most useful information, it is essential that these models contain accurate central nervous system (CNS) tissue properties. Previous material property measurements lack strict control over crucial experimental parameters that may influence material properties and tail to examine any regional variation in the measured response. To address these issues, we measured the material response of two regions of the CNS, the brainstem and the cerebrum. Specifically, adult porcine tissue was subjected to high loading rate mechanical deformation using a custom designed oscillatory shear device. Complex shear moduli were calculated over a range of frequencies (20-200 Hz) at two engineering strain amplitudes (2.5%, and 5.0%).

Carnegie Mellon Driving Research Center, together with ISIM, is presently involved in the design and development of an Advanced Human Factors Research and Driving Training Research Facility. The facility has been designed to address human factors issues and driver training issues. Human factors interests include developing countermeasures for fatigue and driver/vehicle interface issues. Driver training issues include validating the usefulness of simulators for driver training, developing effective curricula and investigating simulator fidelity needed for effective training. A key component of the facility is the Carnegie Mellon TruckSim that will be capable of simulating a variety of commercial and emergency vehicles using interchangeable cabs mounted to a common motion platform. TruckSim's modular configuration will allow for rapid and cost effective design of experiments and training scenarios. A first research program to evaluate fatigue countermeasures is presented as an example.

Traumatic brain injury finite element analyses have evolved from crude geometric representations of the skull and brain system into sophisticated models which take into account distinct anatomical features. However, two distinct finite element modeling approaches have evolved to account for the relative motion that occurs between the skull and cerebral cortex during traumatic brain injury. The first and most common approach assumes that the relative motion can be estimated by representing the cerebrospinal fluid inside the subarachnoid space as a low shear modulus, virtually incompressible solid. The second approach assumes that the relative motion can be approximated by defining a frictional interface between the cerebral cortex and dura mater. This study presents data from an experimental model of traumatic brain injury coupled with finite element analyses to evaluate the modeling approach's ability to predict specific forms of traumatic brain injury.

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.

Driving simulators provide a safe, highly reproducible environment in which to assess driver behavior. Nevertheless, data reduction to standardized metrics can be time-consuming and cumbersome. Further, the validity of the results is challenged by inconsistent definitions of metrics, precluding comparison across studies and integration of data. No established tool has yet been made available and kept current for the systematic reduction of literature-derived safety metrics. The long term goal of this work is to develop DriveLab, a set of widely applicable routines for reducing simulator data to expert-approved metrics. Since Matlab™ is so widely used in the research community, it was chosen as a suitable environment. This paper aims to serve as a case study of data reduction techniques and programming choices that were made for simulator analysis of a specific research project, the Simulated Driving Assessment.

This report discusses the development of brain injury tolerance criteria based on the study of three model systems: the primate, inanimate physical surrogates, and isolated tissue elements. Although we are equally concerned with the neural and neurovascular tissue components of the brain, the report will focus on the former and, in particular, the axonal elements. Under conditions of distributed, impulsive, angularacceleration loading, the primate model exhibits a pathophysiological response ranging from mild cerebral concussion to massive, diffuse white matter damage with prolonged coma. When physical models are subjected to identical loading conditions it becomes possible to map the displacements and calculate the associated strains and stresses within the field simulating the brain. Correlating these experimental models leads to predictive levels of tissue element deformation that may be considered as a threshold for specific mechanisms of injury.

Both traumatic and slow-onset disc herniation can directly compress and/or chemically irritate cervical nerve roots, and both types of root injury elicit pain in animal models of radiculopathy. This study investigated the relative contributions of mechanical compression and chemical irritation of the nerve root to spinal regulation of neuronal activity using several outcomes. Modifications of two proteins known to regulate neurotransmission in the spinal cord, the neuropeptide calcitonin gene-related peptide (CGRP) and glutamate transporter 1 (GLT-1), were assessed in a rat model after painful cervical nerve root injuries using a mechanical compression, chemical irritation or their combination of injury. Only injuries with compression induced sustained behavioral hypersensitivity (p≤0.05) for two weeks and significant decreases (p<0.037) in CGRP and GLT-1 immunoreactivity to nearly half that of sham levels in the superficial dorsal horn.

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.

Validating a traumatic brain injury finite element model is often limited by a lack of extensive animal injury data that may be used to examine the conditions under which the model is accurate. Given that most published reports specify only general descriptions of injury, this study examined potential evaluation strategies and assessed the ability of a finite element model to simulate the general descriptions of injury in an animal model. The results of this study showed that 1) the results from a simplified finite element model could estimate trends that were similar to the injury patterns observed in a set of animal experiments, 2) a parameter (Z parameter), which quantified the comparison process between computational and animal data, estimated trends that would help in the model evaluation process, and 3) a more complete evaluation process would occur if multiple testing methods were included in the evaluation procedure.

Cervical nerve roots are susceptible to compression injuries of various durations. The duration of an applied compression has been shown to contribute to both the onset of persistent pain and also the degree of spinal cellular and molecular responses related to nociception. This study investigated the relationship between peripherally evoked activity in spinal cord neurons during a root compression and the resulting development of axonal damage. Electrically evoked spikes were measured in the spinal cord as a function of time during and after (post-compression) a 15 minute compression of the C7 nerve root. Compression to the root significantly (p=0.035) reduced the number of spikes that were evoked over time relative to sham. The critical time for compression to maximally reduce evoked spikes was 6.6±3.0 minutes. A second study measured the post-compression evoked neuronal activity following compression applied for a shorter, sub-threshold time (three minutes).

Digital mapping tools have become indispensable for road navigation. Applications like Waze and Google Maps harness the power of satellite imagery to provide precise visualization of GPS coordinates. The field advanced significantly in May 2023 with the introduction of dynamic 3D representations of the Earth. Companies such as Cesium now offer Unity3D and Unreal Engine Application Programming Interface that can be applied to geospatial applications. These images are no longer static and offer the opportunity to provide seamless continuous navigation. Driving simulation has been widely used for training and research. We investigate with this project the potential of this new geospatial database as a tool for scenario development to study manual and autonomous driving. We present an in-vehicle driving simulation integration that employs a real steering wheel and pedals from a stationary vehicle as controls.