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Simulation of reach movements is an essential component for proactive ergonomic analysis in digital human modeling and for numerous applications in vehicle design. Most studies on reach kinematics described human movements in static conditions. Earlier studies of reach performance in vibration environments focused mainly on fingertip deviation without considering multi-body dynamics. However, for the proper assessment of reach performance under whole-body vibration exposure, a multi-body biodynamic model needs to be developed. This study analyzes three dimensional reach kinematics of the upper extremity during in-vehicle operations, using a multi-segmental model of the upper body in the vibratory environment. The goals are to identify the characteristics of upper body reach movements and to investigate vibration-induced changes in joint kinematics. Thirteen subjects reached to four target directions in the right hemisphere.

Simulations of humans performing seated reaches require accurate descriptions of the movements of the body segments that make up the torso. Data to generate such simulations were obtained in a laboratory study using industrial, auto, and truck seats. Twelve men and women reached to push-button targets located throughout their right-hand reach envelopes as their movements were recorded using an electromagnetic tracking system. The data illustrate complex patterns of motion that depend on target location and shoulder range of motion. Pelvis motion contributes substantially to seated reach capability. On padded seats, the effective center of rotation of the pelvis is often within the seat cushion below the pelvis rather than at the hips. Lumbar spine motions differ markedly depending on the location of the target. A categorization of reach targets into four zones differentiated by torso kinematics is proposed.

This paper presents results of a five phase study conducted to evaluate touch feel and appearance of door armrest materials. Seven different production door armrests with different material characteristics such as softness, smoothness, compressibility, texture, etc. were evaluated. In the first phase, the subjects seated in a vehicle buck in their preferred seating position with the armrests adjusted at their preferred heights, provided ratings on a number of touch feel and appearance of the door armrest materials using 5-point semantic differential scales. In the second phase, the armrests were presented to each subject in all possible pairs and they were asked to select preferred armrest material in each pair.

New technology emerges daily that enhances traffic and automotive systems. It challenges us to redefine safety in terms of achieving the system's overall goals at a minimal ‘cost’, i.e., loss of life and property. This redefinition requires that we develop the tools needed to understand driving as a decision making activity over a system consisting of driver, vehicle and infrastructure with an ever present and increasing flow of information between them. This even requires that we revisit the very notion of an accident. Loss of life and limb will increasingly be due dysfunctional decision-making, human and automated. This paper describes an approach to the study of driving as a system-based decision making and suggests an approach to the management of dysfunctional decisions. By making explicit, with the aid of game and systems theory, the role of information in driving we suggest a traffic-system based approach to behavioral constraints needed to ensure safety.

The automotive interior suppliers are challenged to develop materials, that not only perform functionally, but also provide the right combination sensory experience (e.g. visual appeal, tactile feeling) and brand differentiation at very competitive costs. Therefore, the objective of this research presented in this paper is to develop a methodology that can be used to measure customer perception of interior materials and to come up with a unique system for assessing value of different interior materials. The overall methodology involves the application of a number of psychophysical measurement methods (e.g. Semantic Differential Scaling) and statistical methods to assess: 1) overall customer perceived quality of materials, 2) elements (or attributes) of perception, and 3) value of materials from OEM's viewpoint in terms of the measurement of perception of quality divided by a measure of cost.

This SAE EDGE™ Research Report explores how the deployment of automated vehicles (AVs) will affect the insurance industry and the principles of liability that underly the structure of insurance in the US. As we trade human drivers for suites of sensors and computers, who (or what) is responsible when there is a crash? The owner of the vehicle? The automaker that built it? The programmer that wrote the code? Insurers have over 100 years of experience and data covering human drivers, but with only a few years’ worth of information on AVs – how can they properly predict the true risks associated with their deployment? Without an understanding of the nature and risks of AVs, how can the government agencies that regulate the insurance industry provide proper oversight? Do the challenges AVs present require a total reworking of our insurance and liability systems, or can our current structures be adapted to fit them with minor modifications?

A research scheme and preliminary results of a pilot study concerning upper body coordination in reach movements is presented. Techniques for multi-joint arm movements were used to obtain the kinematics of each body segment in reach movements to targets spatially distributed in a horizontal plane. Further understanding of the control mechanisms associated with coordination is investigated by combining the information of gaze orientation and body segment movements during reach activities. The implicit sequence of body segments in reach movement can be derived from their kinematic characteristics. Moreover, an identification of phases composing a reach movement is attempted.

The engine operating point (EOP), which is determined by the engine speed and torque, is an important part of a vehicle's powertrain performance and it impacts FC, available propulsion power, and emissions. Predicting instantaneous EOP in real-time subject to dynamic driver behaviour and environmental conditions is a challenging problem, and in existing literature, engine performance is predicted based on internal powertrain parameters. However, a driver cannot directly influence these internal parameters in real-time and can only accommodate changes in driving behaviour and cabin temperature. It would be beneficial to develop a direct relationship between the vehicle-level parameters that a driver could influence in real-time, and the instantaneous EOP. Such a relationship can be exploited to dynamically optimize engine performance.

The Human Motion Simulation Framework is a hierarchical set of algorithms for physical task simulation and analysis. The Framework is capable of simulating a wide range of tasks, including standing and seated reaches, walking and carrying objects, and vehicle ingress and egress. In this paper, model predictions for the terminal postures of standing object transfer tasks are compared to data from 20 subjects with a wide range of body dimensions. Whole body postures were recorded using optical motion capture for one-handed and two-handed object transfers to target destinations at three angles from straight ahead and three heights. The hand and foot locations from the data were input to the HUMOSIM Framework Reference Implementation (HFRI) in the Jack human modeling software. The whole-body postures predicted by the HFRI were compared to the measured postures using a set of measures selected for their importance to ergonomic analysis.

Mcity at the University of Michigan in Ann Arbor provides a realistic off-roadway environment in which to test vehicles and drivers in complex traffic situations. It is intended for testing of various levels of vehicle automation, from advanced driver assistance systems (ADAS) to fully self-driving vehicles. In a recent human factors study of interfaces for teen drivers, we performed parallel experiments in a driving simulator and Mcity. We implemented driving scenarios of moderate complexity (e.g., passing a vehicle parked on the right side of the road just before a pedestrian crosswalk, with the parked vehicle partially blocking the view of the crosswalk) in both the simulator and at Mcity.

The current test methods are insufficient to evaluate and ensure the safety and reliability of vehicle system for all possible dynamic situations including the worst cases such as rollover, spin-out and so on. Although the known NHTSA J-turn and Fish-hook steering maneuvers are applied for the vehicle performance assessment, they are not enough to predict other possible worst case scenarios. Therefore, it is crucial to search for the various worst cases including the existing severe steering maneuvers. This paper includes the procedure to search for other useful worst case based upon the existing worst case scenarios in terms of rollover and its application in simulation basis. The human steering angle is selected as a design variable and optimized to maximize the index function to be expressed in terms of vehicle roll angle. The obtained scenarios were enough to generate the worse cases than NHTSA ones.

Longevity is one of the great achievements of the twentieth century. This paper will explore ways that elderly people can employ technology to enhance their independence, loosely termed “ElderTech.” ElderTech is designed to establish a sustained, long-term investment in research and development (R&D) for technologies that can provide the largest growing population, Americans over the age of 65, with the tools to ensure active aging (maintaining independence, self-reliance, and an enhanced quality of life). It will also promote aging in place (in the home); and will address and ease Medicare's financial burden on the federal government. ElderTech is aimed to establish a technology framework that will ensure that the United States (U.S.) is ready to meet the needs of its older Americans.