Motion Cueing in the Renault Driving Simulator

Motion cueing in a driving simulator is necessary for advanced studies requiring drivers to accurately perceive and control the motion of their vehicle. The Renault Dynamic Simulator uses a 6-axes electro-mechanical mobile platform with an adequate motion control software. The physical and perceptual validity of the motion cueing is analyzed with respect to actual vehicle acceleration data and human self-motion per-ception criteria. Within the actuator displacement limits, it is capable of directly rendering transient vehicle accelerations whereas sustained linear acceleration cues are simulated by a coordinated platform tilt. Accel-eration transients are extracted by high-pass filtering, but a classical implementation based on linear filters may produce artifacts in some key driving situations. A non-linear motion cueing algorithm was developed to anticipate and reduce these false motion cues.     

Motion Cueing in the Renault Driving Simulator

Motion cueing in a driving simulator is necessary for advanced studies requiring drivers to accurately perceive and control the motion of their vehicle. The Renault Dynamic Simulator uses a 6-axes electro-mechanical mobile platform with an adequate motion control software. The physical and perceptual validity of the motion cueing is analyzed with respect to actual vehicle acceleration data and human self-motion per-ception criteria. Within the actuator displacement limits, it is capable of directly rendering transient vehicle accelerations whereas sustained linear acceleration cues are simulated by a coordinated platform tilt. Accel-eration transients are extracted by high-pass filtering, but a classical implementation based on linear filters may produce artifacts in some key driving situations. A non-linear motion cueing algorithm was developed to anticipate and reduce these false motion cues.     

Partial range scaling method based washout algorithm for a vehicle driving simulator and its evaluation

Unlike an actual vehicle, a vehicle driving simulator (VDS) has limited kinematics, workspace, and bounded dynamic characteristics making it very difficult to simulate dynamic motions of an actual vehicle. To solve these problems, a washout algorithm was developed. The developed algorithm restricts the workspace of the VDS to within the kinematic limit and makes the person driving the VDS perceive movement of an actual vehicle. However, the classic washout algorithm contains several problems, such as time delay and the generation of a wrong motion signal caused by characteristics of the filters. So the driver feels “simulator sickness,” such as fatigue, nausea, headache and so on because of differences between the sense of movement of the VDS and that of a real vehicle. In this paper, a partial range scaling method based washout algorithm, including a tilt coordination system, is developed to enhance the perception of motion and reduce simulator sickness. It is verified by a simulation, a survey, and a bio signal analysis using an electrocardiogram (ECG).     

Tuning and objective performance evaluation of a driving simulator to investigate tyre behaviour in on-centre handling manoeuvres

Driving simulation aims at reproducing, within a safe and controlled environment, sensorial stimuli as close to those perceived during the actual drive as possible, in order to induce driving behaviour similar to the real one. This paper illustrates an activity carried out on the driving simulator Virtual Environment for Road Safety, bound for system performance optimisation while dealing with subjective and objective tyres evaluation in the field of on-centre manoeuvres. Such activity can be divided into two main steps. The first one, described herewith, has been focusing on platform motion algorithms tuning and has led to driving simulator objective validation within the on-centre range. Device capability of reproducing dynamics, worked out by the vehicle model, has been thoroughly examined. Simulator sensitivity to a few tyre parameters influencing vehicle lateral dynamics has been analysed too. The second step – calling for the support of experienced drivers – will pursue subjective validation.     

Analysing classes of motion drive algorithms based on paired comparison techniques

A paired comparison experiment using 23 subjects was run on the VIRTTEX driving simulator to compare a lane position based motion drive algorithm (MDA) with a classical MDA for a highway speed, lane change manoeuvre. Two different tuning states of the lane position algorithm and four different tuning states for the classical algorithm were tested. The subjective fidelity of the six different motion cases was compared with each other and a Bradley–Terry model was fit to find the fidelity merit of each case. In addition, the driving performance of the subjects for six motion cases was recorded and compared. The motion-tuning cases were selected such that the trade-off in motion quality between overall motion scaling and motion shape distortion (shape-error), as well as the trade-off between lateral specific force and roll-rate motion errors, could be studied. It was found that when the overall scaling is the same, drivers perform better with the lane position algorithm than with the the classical algorithm. A well-tuned, manoeuvre-specific, classical MDA, however, did achieve a subjective fidelity level on a par with the lane position MDA. A generically tuned classical MDA, however, has a significantly reduced fidelity and driving performance when compared with a lane position algorithm with the same scale factor. A strong trade-off between motion shape-errors and overall motion scaling was found. A small increase in motion cue shape-error, combined with an increase in the scale factor from 0.3 to 0.5, led to improved performance and increased subjective fidelity. The results of the experiment also suggest that simulator motion can be improved by reducing the angular-rate shape-error at the expense of the specific force shape-error (while keeping the total normalised shape-error constant).     

Model predictive driving simulator motion cueing algorithm with actuator-based constraints

The simulator motion cueing problem has been considered extensively in the literature; approaches based on linear filtering and optimal control have been presented and shown to perform reasonably well. More recently, model predictive control (MPC) has been considered as a variant of the optimal control approach; MPC is perhaps an obvious candidate for motion cueing due to its ability to deal with constraints, in this case the platform workspace boundary. This paper presents an MPC-based cueing algorithm that, unlike other algorithms, uses the actuator positions and velocities as the constraints. The result is a cueing algorithm that can make better use of the platform workspace whilst ensuring that its bounds are never exceeded. The algorithm is shown to perform well against the classical cueing algorithm and an algorithm previously proposed by the authors, both in simulation and in tests with human drivers.     

六自由度汽车运动模拟器机构的位置姿态误差分析

六自由度汽车运动模拟器是可进行汽车驾驶逼真模拟的训练和研究用设备。本文利用微分平移和微分放置变换方法，通过误差传递矩阵的求解来探讨模拟器运动台主要误差源与运动台位置姿态误差之间的关系，建立了六自由度模拟器运动台的位置姿态误差模型。     

Human driving data-based design of a vehicle adaptive cruise control algorithm

This paper presents a vehicle adaptive cruise control algorithm design with human factors considerations. Adaptive cruise control (ACC) systems should be acceptable to drivers. In order to be acceptable to drivers, the ACC systems need to be designed based on the analysis of human driver driving behaviour. Manual driving characteristics are investigated using real-world driving test data. The goal of the control algorithm is to achieve naturalistic behaviour of the controlled vehicle that would feel natural to the human driver in normal driving situations and to achieve safe vehicle behaviour in severe braking situations in which large decelerations are necessary. A non-dimensional warning index and inverse time-to-collision are used to evaluate driving situations. A confusion matrix method based on natural driving data sets was used to tune control parameters in the proposed ACC system. Using a simulation and a validated vehicle simulator, vehicle following characteristics of the controlled vehicle are compared with real-world manual driving radar sensor data. It is shown that the proposed control strategy can provide with natural following performance similar to human manual driving in both high speed driving and low speed stop-and-go situations and can prevent the vehicle-to-vehicle distance from dropping to an unsafe level in a variety of driving conditions.     

Motion cueing in high-performance vehicle simulators

The majority of motion cueing algorithms have been developed for passenger car applications, with correspondingly less research dedicated to race-car and high-performance vehicle simulators. In the high-performance context, the focus is on cueing the vehicle's behavioural and handling characteristics, particularly when driving near the limits of performance. In race-car simulators the cueing requirements are therefore quite different, with the problem made all the more challenging by the presence of large accelerations. To understand the drivers' cueing needs, the vehicle's stability and handling response characteristics must be examined near the performance boundary. Frozen-time eigenvalue analyses are used to determine stability and response characteristics across all vehicle operating conditions, including accelerating and braking under cornering, with the results used to determine motion cueing algorithm requirements. Lateral acceleration and yaw cueing filters are designed in order to retain information critical to understanding the vehicle's behaviour on its performance boundary. Cueing filters are then tested, with the help of a professional race-car driver, and are found to provide the cues necessary for the driver to control the vehicle on the limit of performance.     

汽车动态仿真器运动系统的两种驱动算法模拟逼真度分析与比较

本文利用驾驶员前庭系统模拟型分析比较汽车动态仿真器运动系统的两种驱动自满的模拟逼真度。仿真结果表明，自适应Ｗａｓｈｏｕｔ驱动算法比传统Ｗａｓｈｏｕｔ驱动算法具有更高的模拟逼真度，因此，汽车动态仿真器用自适应算法可为驾驶员提供更为逼真的运动感觉 。     

Study on driver model for hybrid truck based on driving simulator experimental results

In this paper, a proposed car-following driver model taking into account some features of both the compensatory and anticipatory model representing the human pedal operation has been verified by driving simulator experiments with several real drivers. The comparison between computer simulations performed by determined model parameters with the experimental results confirm the correctness of this mathematical driver model and identified model parameters. Then the driver model is joined to a hybrid vehicle dynamics model and the moderate car following maneuver simulations with various driver parameters are conducted to investigate influences of driver parameters on vehicle dynamics response and fuel economy. Finally, major driver parameters involved in the longitudinal control of drivers are clarified.     

An adaptive lateral preview driver model

Successful modelling and simulation of driver behaviour is important for the current industrial thrust of computer-based vehicle development. The main contribution of this paper is the development of an adaptive lateral preview human driver model. This driver model template has a few parameters that can be adjusted to simulate steering actions of human drivers with different driving styles. In other words, this model template can be used in the design process of vehicles and active safety systems to assess their performance under average drivers as well as atypical drivers. We assume that the drivers, regardless of their style, have driven the vehicle long enough to establish an accurate internal model of the vehicle. The proposed driver model is developed using the adaptive predictive control (APC) framework. Three key features are included in the APC framework: use of preview information, internal model identification and weight adjustment to simulate different driving styles. The driver uses predicted vehicle information in a future window to determine the optimal steering action. A tunable parameter is defined to assign relative importance of lateral displacement and yaw error in the cost function to be optimized. The model is tuned to fit three representative drivers obtained from driving simulator data taken from 22 human drivers.     

融合预瞄与势场栅格法的紧急避撞驾驶人模型

紧急避障工况下的驾驶人操作具有响应快且动作幅值较大的特点，传统预瞄驾驶人模型已不能适应紧急避障工况的需求，故考虑实际避撞场景开发相应的驾驶人模型就显得尤为必要。针对此种状况，基于驾驶模拟器，结合紧急避撞工况实际驾驶人操纵数据，提出了一种融合预瞄与势场栅格法的紧急避撞驾驶人模型。首先针对紧急避撞工况下车辆运动特点，建立车辆横、纵向耦合非线性动力学模型，并给出其状态空间方程描述；其次，离线仿真分析紧急避撞系统特征，并结合线性二次型最优控制，建立最优曲率预瞄+跟踪误差反馈驾驶人模型；再者，基于紧急避撞工况下真实驾驶人经验转向行为数据，开发基于势场栅格法的驾驶人模型，为进一步提高驾驶人模型对避障行驶工况的适应性，将基于势场栅格法的驾驶人模型与最优曲率预瞄+跟踪误差反馈驾驶人模型进行融合，并基于Sigmoid函数实现两者输出的权重分配；最后，针对所提出的融合预瞄与势场栅格法的驾驶人模型，开展基于避撞台架的驾驶人在环仿真试验以及实车试验。研究结果表明：在紧急避撞工况下，对比最优曲率预瞄+跟踪误差反馈驾驶人模型，融合预瞄与势场栅格法的驾驶人模型输出的转向动作与实际驾驶人行为较为接近，可在保证避障安全性的前提下，兼顾避障路径跟踪精度与车辆行驶的稳定性。     

驾驶员运动感觉及其评价

本文提出三类感觉模拟的评价方法：主观评价，生理心理效应评价及计算机仿真评价，利用前庭系统模型，拟定了驾驶员运动感觉模拟逼真度的仿真评价方程式，并对汽车动态仿真器运动驱动算法进行了仿真评价。仿真结果表明，本文采用的前庭系统模型在所用频率范围内具有可信的精度，用它评价运动感觉模拟逼真度是行之有效的。     

Haptic steering support for driving near the vehicle’s handling limits; skid-pad case

Current vehicle dynamic control systems from simple yaw control to high-end active steering support systems are designed to primarily actuate on the vehicle itself, rather than stimulate the driver to adapt his/her inputs for better vehicle control. The driver though dictates the vehicle’s motion, and centralizing him/her in the control loop is hypothesized to promote safety and driving pleasure. Exploring the above statement, the goal of this study is to develop and evaluate a haptic steering support when driving near the vehicle’s handling limits (Haptic Support Near the Limits; HSNL). The support aims to promote the driver’s perception of the vehicle’s behaviour and handling capacity (the vehicle’s internal model) by providing haptic (torque) cues on the steering wheel. The HSNL has been evaluated in (a) driving simulator tests and (b) tests with a vehicle (Opel Astra G/B) equipped with a variable steering feedback torque system. Drivers attempted to achieve maximum velocity while trying to retain control in a circular skid-pad. In the simulator (a) 25 subjects drove a vehicle model parameterised as the Astra on a dry skid-pad while in (b) 17 subjects drove the real Astra on a wet skid-pad. Both the driving simulator and the real vehicle tests led to the conclusion that the HSNL assisted subjects to drive closer to the designated path while achieving effectively the same speed. With the HSNL the drivers operated the tires in smaller slip angles and hence avoided saturation of the front wheels’ lateral forces and excessive understeer. Finally, the HSNL reduced their mental and physical demand.     

A fast implementation of MPC-based motion cueing algorithms for mid-size road vehicle motion simulators

The use of dynamic driving simulators is constantly increasing in the automotive community, with applications ranging from vehicle development to rehab and driver training. The effectiveness of such devices is related to their capabilities of well reproducing the driving sensations, hence it is crucial that the motion control strategies generate both realistic and feasible inputs to the platform. Such strategies are called motion cueing algorithms (MCAs). In recent years several MCAs based on model predictive control (MPC) techniques have been proposed. The main drawback associated with the use of MPC is its computational burden, that may limit their application to high performance dynamic simulators. In the paper, a fast, real-time implementation of an MPC-based MCA for 9 DOF, high performance platform is proposed. Effectiveness of the approach in managing the available working area is illustrated by presenting experimental results from an implementation on a real device with a 200?Hz control frequency.     

Which factors lead to driving errors? A structural equation model analysis through a driving simulator experiment

As driving error is a main contributory factor of road accidents, its causes and consequences are of great interest in the road safety decision making process. This paper investigates several factors (including driver distraction, driver characteristics and road environment) that affect overall driving error behaviour and estimates a new unobserved variable which underlines driving errors. This estimation is performed with data obtained from a driving simulation experiment in which 95 participants covering all ages were asked to drive under different types of distraction (no distraction, conversation with passenger, cell phone use) in rural and urban road environment, as well as in both low and high traffic conditions. Driving error was then modeled as a latent variable based on several individual driving simulator parameters. Subsequently, the impact of several risk factors such as distraction, driver characteristics as well as road environment on driving error were estimated directly. The results of this complex model reveal that the impact of driver characteristics and area type are the only statistically significant factors affecting the probability of driving errors. Interestingly, neither conversing with a passenger nor talking on the cell phone have a statistically significant impact on driving error behaviour which highlights the importance of the present analysis and more specifically the development of a measure that represents overall driving error behaviour instead of individual driving errors variables.     

In-vehicle display HMI safety evaluation using a driving simulator

Recently, telematics services and in-vehicle display devices such as the CNS (Car Navigation System) have become new causes of traffic accidents. These accidents are caused by ‘Inattention’ from the increase of the driver’s mental workload while he/she is driving. The driver of a vehicle (except for emergency or police vehicles) must not use a hand-held mobile phone while the vehicle is moving. To address this problem, Australia, England, Italy, Brazil and some states in the US have banned the use of hand-held mobile devices during driving. However, there are no restrictions on the use of in-vehicle displays or on the display’s positions. The position of a navigation system in a vehicle should be assessed objectively, and the effect of the position on the driver’s attention should be studied. Some existing research reports that in-vehicle distraction not only leads to reduced speeds and more frequent lane switching, but also more gazing by the driver to the centre of the road. In this study, to develop an assessment method and to propose the proper position of a CNS, an experiment is carried out in a driving simulator environment. Different methods to track the gaze and physical parameters of the driver are used for HMI (Human-Machine Interface) assessment. The experiment is carried out in a driving simulator to observe the glancing distribution during driving according to the position of the navigation system. Fourteen subjects participated in this experiment. Changes in subjects’ physiological signals and glancing distribution rates were collected.     

Experimental evaluation of a vehicle steering assist controller using a driving simulator

Human-in-the-loop driving simulator experiments are conducted to evaluate a proposed robust steering assist controller that is designed on the basis of driver uncertainty modelling. A nominal controller (NC) that is designed without consideration of driver model uncertainty is also tested for comparison. Two types of experiments are proposed: a long driving task with nominal configurations and a short driving task with initially large lateral position error. The data are analysed using both time domain and frequency domain metrics. In the time domain, the standard deviation of lateral position error and percentage of road departure are used. In the frequency domain, the stability margins and crossover frequency are used. The driving simulator results indicate that statistically, the designed robust controller shows improvements in the short driving experiments. The improvements in the long driving experiments are less evident because of driver adaptation. The non-robust NC suffers from high gain and should be avoided. The benefits of considering driver model uncertainty in the design of vehicle steering assist controllers are, therefore, justified.     

Development of a Strategy Model of the Driver in Lane Keeping

Based on vehicle constraints and known human operator characteristics, a strategy model was postulated for describing behavior in the lane keeping task. This model includes nonlinear thresholds operating on vehicle yaw and lateral translation, random input sources to account for spurious driver activity, and smoothing to account for driver response lag. The output of the model is steering wheel position

To determine model parameters and model suitability in describing driver behavior, recordings were made for driver-subjects performing a lane-keeping task in a moving base driving simulator having a computer generated display. A procedure involving both analytic and experimental techniques was then developed for determining the model parameters of each driver

Statistical comparisons and visual inspections made between driver-vehicle and model-vehicle time histories indicate a high degree of correspondence. Models such as these show promise in obtaining a better understanding of driver behavior and driver-vehicle response by incorporating nonlinear elements in the driver model.