Search Results

Motor vehicle crashes involving child Vulnerable Road Users (VRUs) remain a critical public health concern in the United States. While previous studies successfully utilized the crash scenario typology to examine traffic crashes, these studies focus on all types of motor vehicle crashes thus the method might not apply to VRU crashes. Therefore, to better understand the context and causes of child VRU crashes on the U.S. road, this paper proposes a multi-step framework to define crash scenario typology based on the Fatality Analysis Reporting System (FARS) and the Crash Report Sampling System (CRSS). A comprehensive examination of the data elements in FARS and CRSS was first conducted to determine elements that could facilitate crash scenario identification from a systematic perspective. A follow-up context description depicts the typical behavioral, environmental, and vehicular conditions associated with an identified crash scenario.

Making manned and remotely-controlled wheeled and tracked vehicles easier to drive, especially off-road, is of great interest to the U.S. Army. If vehicles are easier to drive (especially closed hatch) or if they are driven autonomously, then drivers could perform additional tasks (e.g., operating weapons or communication systems), leading to reduced crew sizes. Further, poorly driven vehicles are more likely to get stuck, roll over, or encounter mines or improvised explosive devices, whereby the vehicle can no longer perform its mission and crew member safety is jeopardized. HMI technology and systems to support human drivers (e.g., autonomous driving systems, in-vehicle monitors or head-mounted displays, various control devices (including game controllers), navigation and route-planning systems) need to be evaluated, which traditionally occurs in mission-specific (and incomparable) evaluations.

Engaging in visual-manual tasks such as selecting a radio station, adjusting the interior temperature, or setting an automation function can be distracting to drivers. Additionally, if setting the automation fails, driver takeover can be delayed. Traditionally, assessing the usability of driver interfaces and determining if they are unacceptably distracting (per the NHTSA driver distraction guidelines and SAE J2364) involves human subject testing, which is expensive and time-consuming. However, most vehicle engineering decisions are based on computational analyses, such as the task time predictions in SAE J2365. Unfortunately, J2365 was developed before touch screens were common in motor vehicles.

With the evolution of telemetry technology in vehicles, Advanced Automatic Collision Notification (AACN), which detects occupants at risk of serious injury in the event of a crash and triages them to the trauma center quickly, may greatly improve their treatment. An Injury Severity Prediction (ISP) algorithm for AACN was developed using a logistic regression model to predict the probability of sustaining an Injury Severity Score (ISS) 15+ injury. National Automotive Sampling System Crashworthiness Data System (NASS-CDS: 1999-2015) and model year 2000 or later were filtered for new case selection criteria, based on vehicle body type, to match Subaru vehicle category. This new proposed algorithm uses crash direction, change in velocity, multiple impacts, seat belt use, vehicle type, presence of any older occupant, and presence of any female occupant.

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.

Tanker trucks are commonly used for transporting liquid material including chemical and petroleum products. On the one hand, tanker trucks are susceptible to rollover accidents due to the high center of gravity when they are loaded and due to the liquid sloshing effects when the tank is partially filled. On the other hand, tanker truck rollover accidents are among the most dangerous vehicle crashes, frequently resulting in serious to fatal driver injuries and significant property damage, because the liquid cargo is often hazardous and flammable. Therefore, effective schemes for tanker truck rollover avoidance are highly desirable and can bring a considerable amount of societal benefit. Yet, the development of such schemes is challenging, as tanker trucks can operate in various environments and be affected by manufacturing variability, aging, degradation, etc. This paper considers the use of Learning Reference Governor (LRG) for tanker truck rollover avoidance.

Knee airbags (KABs) are one countermeasure in newer vehicles that could influence lower extremity (LEX) injury, the most frequently injured body region in frontal crashes. To determine the effect of KABs on LEX injury for drivers in frontal crashes, the analysis examined moderate or greater LEX injury (AIS 2+) in two datasets. Logistic regression considered six main effect factors (KAB deployment, BMI, age, sex, belt status, driver compartment intrusion). Eighty-five cases with KAB deployment from the Crash Injury Research and Engineering Network (CIREN) database were supplemented with 8 cases from the International Center for Automotive Medicine (ICAM) database and compared to 289 CIREN non-KAB cases. All cases evaluated drivers in frontal impacts (11 to 1 o’clock Principal Direction of Force) with known belt use in 2004 and newer model year vehicles. Results of the CIREN/ICAM dataset were compared to analysis of a similar dataset from NASS-CDS (5441 total cases, 418 KAB-deployed).

Motor vehicle crashes remain the leading cause of death for children. Traditionally, restraint design has focused on the crash phase of the impact with an optimally seated occupant. In order to optimize restrain design for real-world scenarios, research has recently expanded its focus to non-traditional loading conditions including pre-crash positioning and lower speed impacts. The goal of this study was to evaluate the biofidelity of the large omni-directional child (LODC) ATD in non-traditional loading conditions by comparing its response to pediatric volunteer data in low-speed sled tests. Low-speed (2-4 g, 1.9-3.0 m/s) frontal (0°), far-side oblique (60°), and far-side lateral (90°) sled tests, as well as lateral swerving (0.72 g, 0.5 Hz) tests, were conducted using the LODC. The LODC was restrained using a 3-point-belt with an electromechanical motorized seat belt retractor, or pre-pretensioner. Motion capture markers were placed on the head, torso, and belt.

Little information is available on passenger preferences for posture and support in highly reclined seat configurations. To address this gap, a laboratory study was conducted with 24 adult passengers at seat back angles from 23 to 53 degrees. Passenger preferences for head and neck posture with and without head support were recorded. This paper presents the characteristics of the passengers’ preferred head support with respect to thorax, head, and neck posture.

There is a strong evidence that the overrepresentation of teen drivers in motor vehicle crashes is mainly due to their poor hazard perception skills, i.e., they are unskilled at appropriately detecting and responding to roadway hazards. This study evaluates two cuing systems designed to help teens better understand their driving environment. Both systems use directional color-coding to represent different levels of proximity between one’s vehicle and outside agents. The first system provides an overview of the location of adjacent objects in a head-up display in front of the driver and relies on drivers’ focal vision (focal cuing system). The second system presents similar information, but in the drivers’ peripheral vision, by using ambient lights (peripheral cuing system). Both systems were retrofitted into a test vehicle (2014 Toyota Camry). A within-subject experiment was conducted at the University of Michigan Mcity test-track facility.

Motion sickness in road vehicles may become an increasingly important problem as automation transforms drivers into passengers. The University of Michigan Transportation Research Institute has developed a vehicle-based platform to study motion sickness in passenger vehicles. A test-track study was conducted with 52 participants who reported susceptibility to motion sickness. The participants completed in-vehicle testing on a 20-minute scripted, continuous drive that consisted of a series of frequent 90-degree turns, braking, and lane changes at the U-M Mcity facility. In addition to quantifying their level of motion sickness on a numerical scale, participants were asked to describe in words any motion-sickness-related sensations they experienced.

Crash safety researchers have an increased concern regarding the decreased thoracic deflection and the contributing injury causation factors among the elderly population. Sternum fractures are categorized as moderate severity injuries, but can have long term effects depending on the fragility and frailty of the occupant. Current research has provided detail on rib morphology, but very little information on sternum morphology, sternum fracture locations, and mechanisms of injury. The objective of this study is two-fold (1) quantify sternum morphology and (2) document sternum fracture locations using computed tomography (CT) scans and crash data. Thoracic CT scans from the University of Michigan Hospital database were used to measure thoracic depth, manubriosternal joint, sternum thickness and bone density. The sternum fracture locations and descriptions were extracted from 63 International Center for Automotive Medicine (ICAM) crash cases, of which 22 cases had corresponding CT scans.

The purpose of this study was to use detailed medical information to evaluate thoracic injuries in elderly patients in real world frontal crashes. In this study, we used analytic morphomics to predict the effect of torso geometry on rib fracture, a major source of injury for the elderly. Analytic morphomics extracts body features from computed tomography (CT) scans of patients in a semi-automated fashion. Thoracic injuries were examined in front row occupants involved in frontal crashes from the International Center for Automotive Medicine (ICAM) database. Among these occupants, two age groups (age < 60 yr. [Nonelderly] and age ≥ 60 yr. [Elderly]) who suffered severe thoracic injury were analyzed. Regression analyses were conducted to investigate injury outcomes using variables for vehicle, demographics, and morphomics. Compared to the nonelderly group, the elderly group sustained more rib fractures.

Finite element (FE) models are commonly used for automotive crashworthiness design. However, even with increasing speed of computers, the FE-based simulation is still too time-consuming when simulating the complex dynamic process such as vehicle crashworthiness. To improve the computational efficiency, the response surface model, as the surrogate of FE model, has been widely used for crashworthiness optimization design. Before introducing the surrogate model into the design optimization, the surrogate should satisfy the accuracy requirements. However, the bias of surrogate model is introduced inevitably. Meanwhile, it is also very difficult to decide how many samples are needed when building the high fidelity surrogate model for the system with strong nonlinearity. In order to solve the aforementioned problems, the application of a kind of surrogate optimization method called Efficient Global Optimization (EGO) is proposed to conduct the crashworthiness design optimization.

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.

Analyzing dynamic postures of vehicle occupants in various situations is valuable for improving occupant accommodation and safety. Accurate tracking of an occupant’s head is of particular importance because the head has a large range of motion, controls gaze, and may require special protection in dynamic events including crashes. Previous vehicle occupant posture studies have primarily used marker-based optical motion capture systems or multiple video cameras for tracking facial features or markers on the head. However, the former approach has limitations for collecting on-road data, and the latter is limited by requiring intensive manual postprocessing to obtain suitable accuracy. This paper presents an automated on-road head tracking method using a single Microsoft Kinect V2 sensor, which uses a time-of-flight measurement principle to obtain a 3D point cloud representing objects in the scene at approximately 30 Hz.

Electric Power Assisted Steering (EPAS) is widely adopted in modern vehicles to reduce steering effort. It is probable that some EPAS systems will experience a shutdown due to reliability issues stemming from electrical and/or electronic components. In the event of EPAS failure, power assist becomes unavailable and the steering system reverts to a fully manual state, leading to excessive steering torque demands from the driver to maneuver the vehicle at lower speeds, i.e., under 30 mph. This situation has resulted in dozens of reported crashes and several OEM safety recalls in the past few years. Inspired by recent work which utilizes independent driving torque of in-wheel-motor vehicles to reduce steering torque, this paper proposes the use of Differential Braking Assisted Steering (DBAS) to alleviate steep increases in steering torque upon EPAS failure. DBAS requires software upgrades with minimal hardware modification to EPAS, which is preferable for a backup system.

Model Predictive Control (MPC) design methods are becoming popular among automotive control researchers because they explicitly address an important challenge faced by today’s control designers: How does one realize the full performance potential of complex multi-input, multi-output automotive systems while satisfying critical output, state and actuator constraints? Nonlinear MPC (NMPC) offers the potential to further improve performance and streamline the development for those systems in which the dynamics are strongly nonlinear. These benefits are achieved in the MPC framework by using an on-line model of the controlled system to generate the control sequence that is the solution of a constrained optimization problem over a receding horizon.

This paper examines truck driver injury and loss of life in truck crashes related to cab crashworthiness. The paper provides analysis of truck driver fatality and injury in crashes to provide a better understanding of how injury occurs and industry initiatives focused on reducing the number of truck occupant fatalities and the severity of injuries. The commercial vehicle focus is on truck-tractors and single unit trucks in the Class 7 and 8 weight range. The analysis used UMTRI's Trucks Involved in Fatal Accidents (TIFA) survey file and NHTSA's General Estimates System (GES) file for categorical analysis and the Large Truck Crash Causation Study (LTCCS) for a supplemental clinical review of cab performance in frontal and rollover crash types. The paper includes analysis of crashes producing truck driver fatalities or injuries, a review of regulatory development and industry safety initiatives including barriers to implementation.

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.