Development of statistical geometric models for prediction of a driver's hip and eye locations

The Society of Automotive Engineers (SAE) J1517 and J941 models of a driver-selected seat position and a driver's eye location mainly rely on their statistical linear relationships with seat configuration and package variables. Although the SAE models are useful for vehicle interior design, their prediction performance was not provided. The present study was intended to develop accurate prediction models of a driver's hip location (HL) and eye location (EL) based on their statistical geometric relationships with anthropometric dimensions and driving postures. A driving simulation experiment was conducted with 40 Korean drivers (20 males and 20 females) in a seating buck reconfigurable to various package conditions. The anthropometric measurements, HLs, ELs, and joint angles of the participants were collected using an anthropometer, a motion capture system, and a digital human model simulation program. Two types (full model and simplified model) of statistical geometric models (SGMs) for HL and EL prediction were developed by multiple regression analysis of the anthropometric measurements and driving postures on the HLs and ELs. The average adjusted R² and RMSE of the SGMs were .82 (± .06) and 25.7 (±3.3) mm, respectively. The SGMs showed accurate and stable prediction performance because the SGMs additionally incorporated the geometric relationships of HL and EL with anthropometric dimensions and joint angles. The SGMs would be useful to predict the HLs and ELs of drivers with various body sizes and joint angles in occupant packaging.     

Effects of vehicle interior geometry and anthropometric variables on automobile driving posture

The effects of vehicle package, seat, and anthropometric variables on posture were studied in a laboratory vehicle mockup. Participants (68 men and women) selected their preferred driving postures in 18 combinations of seat height, fore-aft steering wheel position, and seat cushion angle. Two seats differing in stiffness and seat back contour were used in testing. Driving postures were recorded using a sonic digitizer to measure the 3D locations of body landmarks. All test variables had significant independent effects on driving posture. Drivers were found to adapt to changes in the vehicle geometry primarily by changes in limb posture, whereas torso posture remained relatively constant. Stature accounts for most of the anthropometrically related variability in driving posture, and gender differences appear to be explained by body size variation. Large intersubject differences in torso posture, which are fairly stable across different seat and package conditions, are not closely related to standard anthropometric measures. The findings can be used to predict the effects of changes in vehicle and seat design on driving postures for populations with a wide range of anthropometric characteristics.     

Statistical prediction of eye locations for drivers of military ground vehicles

In this paper, a statistical model is developed to predict the driver eye locations in military ground vehicles. The data were taken from a study on soldier driving postures and seating positions. The distribution of eye locations is represented by an “eyellipse”, a geometric construction that approximates the distribution of the eye locations using an ellipse. Eyellipses have been widely used for the design of passenger cars and commercial trucks. The location and dimensions of the soldier eyellipse were developed based on the data from 145 male and female soldiers who selected their preferred driving postures in a range of vehicle layouts presented in a vehicle mockup. Driver eye locations were modeled using regression analysis. The model predicts the distribution of eye locations as a function of vehicle interior layout for a soldier population defined by the gender ratio (fraction male) and the distribution of body dimensions within each gender. This is the first eyellipse model taking into account the effects of protective equipment and body-borne gear. The model has broad applicability to the design of tactical, truck-like vehicles with fixed accelerator heel points.     

Simulated effect of driver and vehicle interaction on vehicle interior layout

Digital human modeling is an essential tool to reduce cost and to save time in a design process where humans take the part of users of the design. Considering this phenomenon for a vehicle interior, the importance of the seat track location and adjustment ranges become important. This paper presents the effect of driver and vehicle interaction on vehicle interior layout based on simulation approach. This simulation method includes two optimizations. The first optimization problem is the physics-based seated posture prediction. In order to represent physical drivers, 4,500 virtual drivers are generated based on an anthropometric database-ANSUR. Interaction forces between the digital human and pedal, seat, ground, and steering wheel are incorporated in the physics-based posture prediction. Three different pedal reaction moments (0, 20, and 40 N m) are implemented into the formulation to examine the effect of pedal reaction moment on driver seat location and adjustment ranges. To study the effect of shear forces, the physics-based posture prediction is compared to kinematics-based posture prediction. After posture predictions are completed, individuals' preferred seat locations are used in a second optimization problem to predict the seat track location and adjustment ranges. For a specific vehicle with 20 N m pedal reaction moment, adjustment ranges are predicted as 223 mm and 82 mm in horizontal and vertical directions, respectively. Also, it was shown that shear force due to the interaction between the driver and the seat pan and the pedal reaction moment are both influential to the seat track location and adjustment ranges.Relevance to industryThe simulation model presented in this paper is useful in vehicle and seat design and can be easily used for virtual design assessment in vehicle design.     

“Extra-Ordinary” Ergonomics: How to Accommodate Small and Big Persons,the Disabled and Elderly,Expectant Mothers,and Children

Abstract

Data from a previous study of soldier driving postures and seating positions were analysed to develop statistical models for defining accommodation of driver seating positions in military vehicles. Regression models were created for seating accommodation applicable to driver positions with a fixed heel point and a range of steering wheel locations in typical tactical vehicles. The models predict the driver-selected seat position as a function of population anthropometry and vehicle layout. These models are the first driver accommodation models considering the effects of body armor and body-borne gear. The obtained results can benefit the design of military vehicles, and the methods can also be extended to be utilised in the development of seating accommodation models for other driving environments where protective equipment affects driver seating posture, such as vehicles used by law-enforcement officers and firefighters.

Practitioner Summary: A large-scale laboratory study of soldier driving posture and seating position was designed to focus on tactical vehicle (truck) designs. Regression techniques are utilised to develop accommodation models suitable for tactical vehicles. These are the first seating accommodation models based on soldier data to consider the effects of personal protective equipment and body-borne gear.     

Specifying comfortable driving postures for ergonomic design and evaluation of the driver workspace using digital human models

Specifying comfortable driving postures is essential for ergonomic design and evaluation of a driver workspace. The present study sought to enhance and expand upon several existing recommendations for such postures. Participants (n = 38) were involved in six driving sessions that differed by vehicle class (sedan and SUV), driving venue (laboratory-based and field) or seat (from vehicles ranked high and low by vehicle comfort). Sixteen joint angles were measured in preferred postures to more completely describe driving postures, as were corresponding perceptual responses. Driving postures were found to be bilaterally asymmetric and distinct between vehicle classes, venues, age groups and gender. A subset of preferred postural ranges was identified using a filtering mechanism that ensured desired levels of perceptual responses. Accurate ranges of joint angles for comfortable driving postures, and careful consideration of vehicle and driver factors, will facilitate ergonomic design and evaluation of a driver workspace, particularly when embedded in digital human models.     

Interior layout design of passenger vehicles with RAMSIS

The interior of passenger vehicles and the adapting of interior components to the human body are designed with historical guidelines, based on the experiences of the manufacturer. In contrast to this, the aim of the following study is to create a consistent and theoretically justified procedure to design the interior layout. Using the advantages of virtual design, this will be done with the software tool RAMSIS from scratch. First, four theoretical seating concepts are generated, each fixing one point of the human body (eye point, H‐point, hand point, or heel point) at fixed coordinates for all anthropometric types. Then, the most practical concept is applied together with the geometry of a given vehicle. To generate a realistic and ergonomic seating concept, studies are made concerning the posture of legs and feet in relation to the pedals of the vehicle. The result is a final seating concept with fields of adjustment for seat and steering wheel. © 2005 Wiley Periodicals, Inc. Hum Factors Man 15: 197–212, 2005.     

The effects of joint angle variability and different driving load scenarios on maximum muscle activity – A driving posture simulation study

To date, no studies have been conducted on the main and interaction effects of joint angles on maximum muscle activity in different driving load scenarios. To investigate the influence of joint angle variability on the muscular system, this study calculated maximum muscle activity during three static driving load scenarios through the use of musculoskeletal inverse dynamic simulation. Six joint angles in sagittal plane were varied with reference to reported driving posture angles in the literature. A digital manikin with a height of 180 cm and weight of 70 kg was used with simple muscles and a minimum fatigue criterion for muscle activation optimization. Three static driving load scenarios were simulated: sitting with no external forces except gravity, steering, and pedaling operation. Prediction models were developed for each driving load scenario using Least Squares Support Vector Machine. Finally, the Pareto optimization method was applied for multi-objective optimization combining the three developed models.The results indicate that the developed models can be used for the prediction of simulated maximum muscle activity. The six joint angles explain a higher percentage of maximum muscle activity variance in the steering and pedaling operation scenarios compared to the sitting scenario. The six joint angles differ in their main and interaction effects on maximum muscle activity depending on the driving load scenario. The optimum joint angle values of the driving posture depend on the driving load scenarios. The different driving postures based on minimum maximum muscle activity are presented for the three driving load scenarios.Relevance to industryThe results of this study can be utilized in establishing driving posture simulation models to improve vehicle interiors during the early development stage. Furthermore, the results of this study can provide base data for the development of a tool for real driving posture evaluation of maximum muscle activity.     

Ted Megaw: an appreciation

Driver workspace design and evaluation is, in part, based on assumed driving postures of users and determines several ergonomic aspects of a vehicle, such as reach, visibility and postural comfort. Accurately predicting and specifying standard driving postures, hence, are necessary to improve the ergonomic quality of the driver workspace. In this study, a statistical clustering approach was employed to reduce driving posture simulation/prediction errors, assuming that drivers use several distinct postural strategies when interacting with automobiles. 2-D driving postures, described by 16 joint angles, were obtained from 38 participants with diverse demographics (age, gender) and anthropometrics (stature, body mass) and in two vehicle classes (sedans and SUVs). Based on the proximity of joint angle sets, cluster analysis yielded three predominant postural strategies in each vehicle class (i.e. ‘lower limb flexed’, ‘upper limb flexed’ and ‘extended’). Mean angular differences between clusters ranged from 3.8 to 52.4° for the majority of joints, supporting the practical relevance of the distinct clusters. The existence of such postural strategies should be considered when utilising digital human models (DHMs) to enhance and evaluate driver workspace design ergonomically and proactively.

Statement of Relevance: This study identified drivers' distinct postural strategies, based on actual drivers' behaviours. Such strategies can facilitate accurate positioning of DHMs and hence help design ergonomic driver workspaces.     

Three‐dimensional analysis of a driver‐passenger vehicle interface

This article presents a method of analyzing how drivers' anthropometric data are best accommodated by a specific driver‐vehicle interface. Three‐dimensional (3‐D) manikins with 18 links were developed using anthropometric data for the U.S. 95th percentile male and 5th percentile female. In addition, an adjustable seating buck was constructed to control 7 package variables. After the manikins were positioned in each driving environment, 3‐D Cartesian coordinates for the manikins' articulations were determined using a coordinate measuring machine. The data were then converted into joint angles to suggest suitable driving environments that consider appropriate driving postures. © 2004 Wiley Periodicals, Inc. Hum Factors Man 14: 269–284, 2004.     

A memory-based model for planning target reach postures in the presence of obstructions

Existing posture prediction and motion simulation models generally lack the capability of simulating human obstruction avoidance during target reach. This compromises the utility of digital human models for ergonomics, as many design problems involve interactions between humans and obstructions. To address this problem, this paper presents a novel memory-based posture planning (MBPP) model, which plans reach postures that avoid obstructions. In this model, the task space is partitioned into small regions called cells. For a given human figure, each cell is linked to a memory that stores various alternative postures for reaching the cell. When a posture planning problem is given in terms of a target and an obstruction configuration, the model examines postures belonging to the relevant cell, selects collision-free ones and modifies them to exactly meet the hand target acquisition constraint. Simulation results showed that the MBPP model is capable of rapidly and robustly planning reach postures for various scenarios.     

A memory-based model for planning target reach postures in the presence of obstructions

Existing posture prediction and motion simulation models generally lack the capability of simulating human obstruction avoidance during target reach. This compromises the utility of digital human models for ergonomics, as many design problems involve interactions between humans and obstructions. To address this problem, this paper presents a novel memory-based posture planning (MBPP) model, which plans reach postures that avoid obstructions. In this model, the task space is partitioned into small regions called cells. For a given human figure, each cell is linked to a memory that stores various alternative postures for reaching the cell. When a posture planning problem is given in terms of a target and an obstruction configuration, the model examines postures belonging to the relevant cell, selects collision-free ones and modifies them to exactly meet the hand target acquisition constraint. Simulation results showed that the MBPP model is capable of rapidly and robustly planning reach postures for various scenarios.     

A statistical model including age to predict passenger postures in the rear seats of automobiles

Few statistical models of rear seat passenger posture have been published, and none has taken into account the effects of occupant age. This study developed new statistical models for predicting passenger postures in the rear seats of automobiles. Postures of 89 adults with a wide range of age and body size were measured in a laboratory mock-up in seven seat configurations. Posture-prediction models for female and male passengers were separately developed by stepwise regression using age, body dimensions, seat configurations and two-way interactions as potential predictors. Passenger posture was significantly associated with age and the effects of other two-way interaction variables depended on age. A set of posture-prediction models are presented for women and men, and the prediction results are compared with previously published models. This study is the first study of passenger posture to include a large cohort of older passengers and the first to report a significant effect of age for adults. The presented models can be used to position computational and physical human models for vehicle design and assessment.

Practitioner Summary:

The significant effects of age, body dimensions and seat configuration on rear seat passenger posture were identified. The models can be used to accurately position computational human models or crash test dummies for older passengers in known rear seat configurations.     

基于虚拟现实的装甲车辆运动仿真研究

装甲车行驶中车体姿态的仿真是模拟驾驶训练系统的关键技术.为实现不同地形下基于虚拟现实的装甲车辆驾驶模拟,本文提出了基于虚拟现实的装甲车辆运动仿真方法.首先,构建装甲车辆的结构、外形和真实的地形,采用第一和第三视角同步显示驾驶场景;其次,建立车辆行驶的动力学模型,求解车身在不同地形下的车身姿态数据;最后,通过Unity3D引擎实现整个场景和车体姿态的动态渲染.通过实验证明,该方法能够准确的模拟各种地形条件下的车体姿态,真实的仿真车辆的运行状态.     

Predicting peak pinch strength: Artificial neural networks vs. regression

Cumulative trauma disorders (CTDs) of the upper extremities are one of the major ergonomics areas of research. Pinching is a common risk factor associated with the development of hand/wrist CTDs. The capacity standards of peak pinch strength for various postures are needed to design the tasks in harmony with the workers. This paper describes the formulation, building and comparison of pinch strength prediction models that were obtained using two approaches: Statistical and artificial neural networks (ANN). Statistical and ANN models were developed to predict the peak chuck pinch strength as a function of different combinations of five elbow and seven shoulder flexion angles, and several anthropometric and physiological variables. The two modeling approaches were compared. The results indicate ANN models to provide more accurate predictions over the standard statistical models.     

Effect of posture positions on the evaporative resistance and thermal insulation of clothing

Evaporative resistance and thermal insulation of clothing are important parameters in the design and engineering of thermal environments and functional clothing. Past work on the measurement of evaporative resistance of clothing was, however, limited to the standing posture with or without body motion. Information on the evaporative resistance of clothing when the wearer is in a sedentary or supine posture and how it is related to that when the wearer is in a standing posture is lacking. This paper presents original data on the effect of postures on the evaporative resistance of clothing, thermal insulation and permeability index, based on the measurements under three postures, viz. standing, sedentary and supine, using the sweating fabric manikin-Walter. Regression models are also established to relate the evaporative resistance and thermal insulation of clothing under sedentary and supine postures to those under the standing posture. The study further shows that the apparent evaporated resistances of standing and sedentary postures measured in the non-isothermal condition are much lower than those in the isothermal condition. The apparent evaporative resistances measured using the mass loss method are generally lower than those measured using the heat loss method due to moisture absorption or condensation within clothing. STATEMENT OF RELEVANCE: The thermal insulation and evaporative resistance values of clothing ensembles under different postures are essential data for the ergonomics design of thermal environments (e.g. indoors or a vehicle's interior environment) and functional clothing. They are also necessary for the prediction of thermal comfort or duration of exposure in different environmental conditions.     

Design and evaluation of a model predictive vehicle control algorithm for automated driving using a vehicle traffic simulator

This paper describes the design and evaluation of a model predictive control algorithm for automated driving on a motorway using a vehicle traffic simulator. For the development of a highly automated driving control algorithm, motion planning is necessary to satisfy driving condition in various road traffic situations. There are two key issues in motion planning of automated driving vehicles. One of the key issues is how to handle potentially dangerous situations that could occur in order to guarantee the safety of vehicles. The second key issue is how to guarantee the disturbance rejection of the controller under model uncertainties and external disturbances. To improve safety with respect to the future behaviors of subject vehicles, not the current states but rather the predicted behaviors of surrounding vehicles should be considered. The desired driving mode and a safe driving envelope are determined based on the probabilistic prediction of surrounding vehicles behaviors over a finite prediction horizon. To obtain the desired steering angle and longitudinal acceleration for maintaining the subject vehicle in the safe driving envelope during a finite prediction horizon, a motion planning controller is designed based on an model predictive control (MPC) approach. The developed control algorithm has been successfully implemented on a vehicle electronic control unit (ECU). The proposed control algorithm has been evaluated on a real-time vehicle traffic simulator. The throttle, brake, and steering control inputs and the controlled vehicle behavior have been compared to those of manual driving.     

Human shape correspondence with automatically predicted landmarks

We consider the problem of computing accurate point-to-point correspondences among a set of human bodies in similar posture using a landmark-free approach. The approach learns the locations of the anthropometric landmarks present in a database of human models in similar postures and uses this knowledge to automatically predict the locations of these anthropometric landmarks on a newly available scan. The predicted landmarks are then used to compute point-to-point correspondences between a template model and the newly available scan. This study conducts a large-scale evaluation to examine the accuracy of the computed correspondences. Furthermore, we show that the correspondences are accurate enough for the application of motion transfer.