A statistical method for predicting automobile driving posture

A new model for predicting automobile driving posture is presented. The model, based on data from a study of 68 men and women in 18 vehicle package and seat conditions, is designed for use in posturing the human figure models that are increasingly used for vehicle interior design. The model uses a series of independent regression models, coupled with data-guided inverse kinematics, to fit a whole-body linkage. An important characteristic of the new model is that it places greatest importance on prediction accuracy for the body locations that are most important for vehicle interior design: eye location and hip location. The model predictions were compared with the driving postures of 120 men and women in five vehicles. Errors in mean eye location predictions in the vehicles were typically less than 10 mm. Prediction errors were largely independent of anthropometric variables and vehicle layout. Although the average posture of a group of people can be predicted accurately, individuals' postures cannot be predicted precisely because of interindividual posture variance that is unrelated to key anthropometric variables. The posture prediction models developed in this research can be applied to posturing computer-rendered human models to improve the accuracy of ergonomic assessments of vehicle interiors.     

“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.     

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.     

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.     

The locations of headlamps and driver eye positions in vehicles sold in the USA

Information about the locations of headlamps and driver eye positions is important in estimating the performance of a variety of traffic-safety equipment, such as retroreflective traffic signs and markings, rearview mirrors, and headlamps. Driver eye height is also an important factor in determining safe sight distances on vertical curves. However, no comprehensive database concerning the locations of headlamps and driver eye positions exists for the current US fleet. The present study was designed to obtain such information for vehicles currently sold in the USA. The information was derived from the 15 bestselling cars and the 15 best-selling light trucks and vans. These 30 vehicles represent 52% of all vehicles sold in the USA in 1995. For headlamp locations, actual vehicle measurements were made. For driver eye positions, dimensional information provided by vehicle manufacturers was used to estimate the location of the driver seating reference point. A computer model that predicts the relationship of the driver eye position to the driver seating reference point was then used to calculate driver eye positions. The obtained information includes sales-weighted mean dimensions for the locations of headlamps and driver eye positions, calculated separately for cars and for light trucks and vans.     

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.     

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.     

基于道路结构的无定位车辆出库转向控制方法

针对地下车库内定位信号差,无人驾驶车辆不能获得自身位置信息来完成在全局参考系中路径规划的问题,提出利用道路边沿的几何结构,从理论上推导转向控制的方法以完成车辆的出库。首先,给出车辆的驾驶场景和用于仿真的低速车辆模型;然后根据道路边沿数据,从理论上推导车辆相对于道路的位姿以及转弯处的转向曲率,并给出车辆在各路段的转向角控制方法;最后,在获取理想与非理想的道路边沿数据情况下,分别仿真采用该方法的车辆行驶状况。仿真结果表明,在道路边沿测量误差小于±20 cm的情况下,方法可以实现无定位的自主驾驶。     

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 Sensorless State Estimation for A Safety-Oriented Cyber-Physical System in Urban Driving: Deep Learning Approach

In today’s modern electric vehicles, enhancing the safety-critical cyber-physical system (CPS)’s performance is necessary for the safe maneuverability of the vehicle. As a typical CPS, the braking system is crucial for the vehicle design and safe control. However, precise state estimation of the brake pressure is desired to perform safe driving with a high degree of autonomy. In this paper, a sensorless state estimation technique of the vehicle’s brake pressure is developed using a deep-learning approach. A deep neural network (DNN) is structured and trained using deep-learning training techniques, such as, dropout and rectified units. These techniques are utilized to obtain more accurate model for brake pressure state estimation applications. The proposed model is trained using real experimental training data which were collected via conducting real vehicle testing. The vehicle was attached to a chassis dynamometer while the brake pressure data were collected under random driving cycles. Based on these experimental data, the DNN is trained and the performance of the proposed state estimation approach is validated accordingly. The results demonstrate high-accuracy brake pressure state estimation with RMSE of 0.048 MPa.      

基于滚动优化和能量回收的V2I电动汽车决策

通过对标准新欧洲汽车法规循环(NEDC)工况的分析,提取出NEDC工况中的实时交通信息,分析不同驾驶状态对于车辆能耗的影响,提出一种新的基于实时交通信息的适用于V2I车辆的测试工况;结合电动汽车能量回收的优点,考虑电池-电机-制动特性约束,设计多源信息融合框架下的制动力分配策略;结合模型预测控制(MPC)的滚动优化思想提出MPC软约束框架下的电动汽车V2I最优控制策略;在AMESim & Simulink联合仿真平台上进行高精度纯电动车整车建模和MPC最优控制器的设计;对优化后车辆和未优化的车辆进行仿真对比验证,结果表明:结合道路交通信息进行最优决策的V2I纯电动车辆可有效降低车辆运行中的启停频率,减少整车能耗、车辆加速度和冲击度幅度,并显著提高整车经济性和舒适性.     

配送选址-多车型运输路径优化问题及求解算法

考虑到不同车型、车辆容量、时间窗等约束,研究了配送选址-多车型运输路径优化问题,采用分解法进行问题分析,建立数学模型.首先应用改进聚类分析模型确定配送中心的最佳位置与服务客户群,然后设计遗传算法进行求解.算法比较及算例测试表明它是求解选址-多车型运输路径优化问题的一种有效方法.     

基于稀疏帧检测的交通目标跟踪

为了获取高速公路交通视频中目标车辆的行驶轨迹,提出一种基于视频的多目标车辆跟踪及实时轨迹分布算法,为交通管理系统和交通决策提供目标车辆交通信息.首先,使用YOLOv4算法检测目标车辆位置及置信度.其次,在不同场景条件下,使用提出的基于稀疏帧检测的跟踪方法,结合KCF跟踪算法,将车辆数据进行关联获取完整轨迹.最后,用车辆分布图和交通场景俯视图显示轨迹,便于交通管理与分析.实验结果表明,提出的跟踪方法在车辆跟踪中有较高的跟踪正确率,同时基于稀疏帧检测的跟踪方法处理速度也较快,实时轨迹分布正确反映了真实场景的车道信息以及目标车辆运动信息.     

Model‐based Prediction of Skid‐steer Robot Kinematics Using Online Estimation of Track Instantaneous Centers of Rotation

This paper presents a kinematic extended Kalman filter (EKF) designed to estimate the location of track instantaneous centers of rotation (ICRs) and aid in model‐based motion prediction of skid‐steer robots. Utilizing an ICR‐based kinematic model has resulted in impressive odometry estimates for skid‐steer movement in previous works, but estimation of ICR locations was performed offline on recorded data. The EKF presented here utilizes a kinematic model of skid‐steer motion based on ICR locations. The ICR locations are learned by the filter through the inclusion of position and heading measurements. A background on ICR kinematics is presented, followed by the development of the ICR EKF. Simulation results are presented to aid in the analysis of noise and bias susceptibility. The experimental platforms and sensors are described, followed by the results of filter implementation. Extensive field testing was conducted on two skid‐steer robots, one with tracks and another with wheels. ICR odometry using learned ICR locations predicts robot position with a mean error of ?0.42 m over 40.5 m of travel during one tracked vehicle test. A test consisting of driving both vehicles approximately 1,000 m shows clustering of ICR estimates for the duration of the run, suggesting that ICR locations do not vary significantly when a vehicle is operated with low dynamics.     

基于区块链的危险驾驶地图数据评估模型

在车联网中车辆与周边道路环境进行无线通信与数据交互,使车辆传感器、行人、道路单元之间实现信息互联,但大量的数据通信导致车辆运行环境复杂,车辆在行驶过程中不可避免地会遇到各种危险场景,从而引发车辆安全问题。为确保车辆数据可信及行车安全,建立基于区块链技术的危险驾驶地图数据评估模型。通过传感器实时记录环境数据,将车辆位置、速度等关键数据以及个人隐私信息加密上链实现交易传输,利用智能合约技术进行自动化脚本处理,随后执行共识将交易信息扩散到全部节点,节点承认交易合法后写入区块链。同时对车辆地图交互数据进行危险评估,并将评级结果及其对应操作反馈给车辆,使得车辆可在障碍物、网络攻击等场景下完成应急处理。实验结果表明,该模型能够根据不同危险场景对车辆进行危险评估与规避,确保车辆安全运行。     

Physical workload and psychosocial working conditions in Swedish pig transport drivers

Working conditions of 20 Swedish pig transport drivers (PTD) were assessed by a questionnaire, a workshop, and recorded postures and movements during on-farm loading, driving, unloading at abattoir and vehicle cleaning. High arm positions and high frequencies of shoulder problems indicated an excessive physical load on shoulders. Extreme crouching postures inside vehicles and high frequencies of lower back problems indicated high load on the back of PTDs. Inadequate design of on-farm loading areas was associated with knee discomfort. Observed variation in workload between PTDs could be explained by differences in physical working environment and pig handling practices. PTDs reported high job satisfaction and commitment although conflicts with farmers and official veterinarians, as well as regulatory conflicts were emphasized. We conclude that issues of inadequate farm and abattoir facilities, stakeholder conflicts and insufficient training in animal handling need to be addressed to ensure sustainable PTD working conditions.     

结合状态机和动态目标路径的无人驾驶决策仿真

目的 决策系统是无人驾驶技术的核心研究之一。已有决策系统存在逻辑不合理、计算效率低、应用场景局限等问题,因此提出一种动态环境下无人驾驶路径决策仿真。方法 首先,基于规则模型构建适于无人驾驶决策系统的交通有限状态机;其次,针对交通动态特征,提出基于统计模型的动态目标路径算法计算状态迁移风险;最后,将交通状态机和动态目标路径算法有机结合,设计出一种基于有限状态机的无人驾驶动态目标路径模型,适用于交叉口冲突避免和三车道换道行为。将全速度差连续跟驰模型运用到换道规则中,并基于冲突时间提出动态临界跟车距离。结果 为验证模型的有效性和高效性,对交通环境进行虚拟现实建模,模拟交叉口通行和三车道换道行为,分析文中模型对车流量和换道率的影响。实验结果显示,在交叉口通行时,自主车辆不仅可以检测冲突还可以根据风险评估结果执行安全合理的决策。三车道换道时,自主车辆既可以实现紧急让道,也可以通过执行换道达成自身驾驶期望。通过将实测数据和其他两种方法对比,当车流密度在0.20.5时,本文模型的平均速度最高分别提高32 km/h和22 km/h。当车流密度不超过0.65时,本文模型的换道成功率最高分别提升37%和25%。结论 实验结果说明本文方法不仅可以在动态城区环境下提高决策安全性和正确性,还可以提高车流量饱和度,缓解交通堵塞。     

Postural implications of obtaining line-of-sight for seated operators of underground mining load-haul-dump vehicles

Operators of load-haul-dump (LHD) vehicles use awkward postures that may be held statically and at extreme ranges of motion for long shift periods to spot hazards in underground mining. This study examined postural variables associated with three amounts of seat rotation intended to maximize line-of-sight during forward driving. Three different models, representing the 1st, 50th and 99th percentile male for height and weight, were positioned with appropriate hand and foot constraints in the virtual LHD cab modelled in Classic JACK v4.0. A total of 15 virtual movement strategies were developed to model the postural behaviour of typical workers and each virtual subject was tested, first with the seat in a neutral 0 degrees position and then with it rotated counter-clockwise to 20 degrees and 45 degrees . Results revealed that reductions in trunk rotation, trunk lateral bend and neck rotation were associated with the seat rotation intervention. The general relationship observed was that as seat rotation increased, view of critical visual attention locations and visible line-of-sight area increased while postural load variables decreased. For the most part, 20 degres of seat rotation was beneficial but 45 dgrees produced significantly greater changes to postural load and visible visual attention locations.     

基于长短期记忆网络的高速公路车辆变道轨迹预测模型

高速公路车辆车速、车距、行驶方向等因素都是动态变化的,受外界环境干扰,采集到的目标车辆状态特征数据可能存在噪声,导致车辆变道轨迹预测存在误差,为此提出基于长短期记忆网络的高速公路车辆变道轨迹预测模型,有效预测高速公路车辆变道轨迹,改善车辆行驶条件,保障其安全运行。通过激光雷达、GPS等装置采集目标车辆交通数据,将其合理组合成目标车辆状态观测特征向量,并构建相应的特征向量矩阵,将所构建目标车辆状态观测特征向量矩阵作为1层卷积神经网路输入,提取目标车辆状态观测特征向量潜在特征后,以1层卷积神经网络输出结果为双向长短期记忆网络有效输入,经过无数次模型训练后,输出目标车辆变道轨迹预测结果。实验结果表明:该模型可有效预测高速公路车辆变道轨迹,预测出的轨迹横纵坐标误差极低,能够得到较为理想的高速公路车辆变道轨迹预测结果。