An investigation on motor-driven power steering-based crosswind disturbance compensation for the reduction of driver steering effort

This paper describes a lateral disturbance compensation algorithm for an application to a motor-driven power steering (MDPS)-based driver assistant system. The lateral disturbance including wind force and lateral load transfer by bank angle reduces the driver's steering refinement and at the same time increases the possibility of an accident. A lateral disturbance compensation algorithm is designed to determine the motor overlay torque of an MDPS system for reducing the manoeuvreing effort of a human driver under lateral disturbance. Motor overlay torque for the compensation of driver's steering torque induced by the lateral disturbance consists of human torque feedback and feedforward torque. Vehicle–driver system dynamics have been investigated using a combined dynamic model which consists of a vehicle dynamic model, driver steering dynamic model and lateral disturbance model. The human torque feedback input has been designed via the investigation of the vehicle–driver system dynamics. Feedforward input torque is calculated to compensate additional tyre self-aligning torque from an estimated lateral disturbance. The proposed compensation algorithm has been implemented on a developed driver model which represents the driver's manoeuvreing characteristics under the lateral disturbance. The developed driver model has been validated with test data via a driving simulator in a crosswind condition. Human-in-the-loop simulations with a full-scale driving simulator on a virtual test track have been conducted to investigate the real-time performance of the proposed lateral disturbance compensation algorithm. It has been shown from simulation studies and human-in-the-loop simulation results that the driver's manoeuvreing effort and a lateral deviation of the vehicle under the lateral disturbance can be significantly reduced via the lateral disturbance compensation algorithm.     

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.     

The characteristic velocity stability indicator for passenger cars

Driver assistance systems have received increased attention as market demands have pushed for improved automotive safety. These systems are designed to aid the driver by preventing any unstable or unpredictable vehicle behaviour. One global indicator for stability and driving conditions could help to manage the control algorithms and driver warning subroutines. Another problem which could be solved by a precise driving situation indicator is evaluating new vehicles during test drives. After a short introduction to a linear lateral vehicle model, an analytical approach for an online calculation of different driving conditions (i.e., stability, understeering, oversteering, and neutralsteering) is given. A characteristic velocity stability indicator is defined, which allows online computation of the present driving condition. Results are then checked against real measurements of a test vehicle.     

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。     

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.     

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.     

Application of Elementary Neural Networks and Preview Sensors for Representing Driver Steering Control Behaviour

This paper demonstrates the use of elementary neural networks for modelling and representing driver steering behaviour in path regulation control tasks. Areas of application include uses by vehicle simulation experts who need to model and represent specific instances of driver steering control behaviour, potential on-board vehicle technologies aimed at representing and tracking driver steering control behaviour over time, and use by human factors specialists interested in representing or classifying specific families of driver steering behaviour. Example applications are shown for data obtained from a driver/vehicle numerical simulation, a basic driving simulator, and an experimental on-road test vehicle equipped with a camera and sensor processing system.     

The characteristic velocity stability indicator for passenger cars

Driver assistance systems have received increased attention as market demands have pushed for improved automotive safety. These systems are designed to aid the driver by preventing any unstable or unpredictable vehicle behaviour. One global indicator for stability and driving conditions could help to manage the control algorithms and driver warning subroutines. Another problem which could be solved by a precise driving situation indicator is evaluating new vehicles during test drives. After a short introduction to a linear lateral vehicle model, an analytical approach for an online calculation of different driving conditions (i.e., stability, understeering, oversteering, and neutralsteering) is given. A characteristic velocity stability indicator is defined, which allows online computation of the present driving condition. Results are then checked against real measurements of a test vehicle.     

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.     

Vehicle dynamics and road curvature estimation for lane departure warning system using robust fuzzy observers: experimental validation

This paper describes a new approach to estimate vehicle dynamics and the road curvature in order to detect vehicle lane departures. This method has been evaluated through an experimental set-up using a real test vehicle equipped with the RT2500 inertial measurement unit. Based on a robust unknown input fuzzy observer, the road curvature is estimated and compared to the vehicle trajectory curvature. The difference between the two curvatures is used by the proposed lane departure detection algorithm as the first driving risk indicator. To reduce false alarms and take into account driver corrections, a second driving risk indicator based on the steering dynamics is considered. The vehicle nonlinear model is deduced from the vehicle lateral dynamics and road geometry and then represented by an uncertain Takagi–Sugeno fuzzy model. Taking into account the unmeasured variables, an unknown input fuzzy observer is proposed. Synthesis conditions of the proposed fuzzy observer are formulated in terms of linear matrix inequalities using the Lyapunov method.     

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 curving ACC system with coordination control of longitudinal car-following and lateral stability

The paper presents a curving adaptive cruise control (ACC) system that is coordinated with a direct yaw-moment control (DYC) system and gives consideration to both longitudinal car-following capability and lateral stability on curved roads. A model including vehicle longitudinal and lateral dynamics is built first, which is as discrete as the predictive model of the system controller. Then, a cost function is determined to reflect the contradictions between vehicle longitudinal and lateral dynamics. Meanwhile, some I/O constraints are formulated with a driver permissible longitudinal car-following range and the road adhesion condition. After that, desired longitudinal acceleration and desired yaw moment are obtained by a linear matrix inequality based robust constrained state feedback method. Finally, driver-in-the-loop tests on a driving simulator are conducted and the results show that the developed control system provides significant benefits in weakening the impact of DYC on ACC longitudinal car-following capability while also improving lateral stability.     

Role of steering wheel feedback on driver performance: driving simulator and modeling analysis

When driving in curves, how do drivers use the force appearing on the steering wheel? As it carries information related to lateral acceleration, this force could be necessary for drivers to tune their internal model of vehicle dynamics; alternatively, being opposed to the drivers' efforts, it could just help them stabilize the steering wheel position. To assess these two hypotheses, we designed an experiment on a motion-based driving simulator. The steering characteristics of the vehicle were modified in the course of driving, unknown to drivers. Results obtained with standard drivers showed a surprisingly wide range of adaptation, except for exaggerated modifications of the steering force feedback. A two-level driver model, combining a preview of vehicle dynamics and a neuromuscular steering control, reproduces these experimental results qualitatively and indicates that adaptation occurs at the haptic level rather than in the internal model of vehicle dynamics. This effect is related to other theories on the manual control of dynamics systems, wherein force feedback characteristics are abstracted at the position control level. This research also illustrates the use of driving simulation for the study of driver behavior and future intelligent steering assistance systems.     

A path-following driver–vehicle model with neuromuscular dynamics,including measured and simulated responses to a step in steering angle overlay

An existing driver–vehicle model with neuromuscular dynamics is improved in the areas of cognitive delay, intrinsic muscle dynamics and alpha–gamma co-activation. The model is used to investigate the influence of steering torque feedback and neuromuscular dynamics on the vehicle response to lateral force disturbances. When steering torque feedback is present, it is found that the longitudinal position of the lateral disturbance has a significant influence on whether the driver’s reflex response reinforces or attenuates the effect of the disturbance. The response to angle and torque overlay inputs to the steering system is also investigated. The presence of the steering torque feedback reduced the disturbing effect of torque overlay and angle overlay inputs. Reflex action reduced the disturbing effect of a torque overlay input, but increased the disturbing effect of an angle overlay input. Experiments on a driving simulator showed that measured handwheel angle response to an angle overlay input was consistent with the response predicted by the model with reflex action. However, there was significant intra- and inter-subject variability. The results highlight the significance of a driver’s neuromuscular dynamics in determining the vehicle response to disturbances.     

Role of steering wheel feedback on driver performance: driving simulator and modeling analysis

When driving in curves, how do drivers use the force appearing on the steering wheel? As it carries information related to lateral acceleration, this force could be necessary for drivers to tune their internal model of vehicle dynamics; alternatively, being opposed to the drivers' efforts, it could just help them stabilize the steering wheel position. To assess these two hypotheses, we designed an experiment on a motion-based driving simulator. The steering characteristics of the vehicle were modified in the course of driving, unknown to drivers. Results obtained with standard drivers showed a surprisingly wide range of adaptation, except for exaggerated modifications of the steering force feedback. A two-level driver model, combining a preview of vehicle dynamics and a neuromuscular steering control, reproduces these experimental results qualitatively and indicates that adaptation occurs at the haptic level rather than in the internal model of vehicle dynamics. This effect is related to other theories on the manual control of dynamics systems, wherein force feedback characteristics are abstracted at the position control level. This research also illustrates the use of driving simulation for the study of driver behavior and future intelligent steering assistance systems.     

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.     

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.     

Advanced Steering System Adaptable to Lateral Control Task and Driver's Intention

This paper proposes an advanced steering system that adaptively varies the static gain and dynamics of the steering system. The steering system gain is adjusted, depending on whether the driver is in an aggressive or leisurely driving mood. The steering system dynamics is so designed that the command mode of the steering system will be either a rate-command or an attitude-command according to the lateral control task performed by the driver. The recognition system for lateral control tasks, a lane-following or lane-change task is proposed. The findings of simulator tests indicate proposed advanced steering system would remarkably improve the vehicle handling qualities.     

汽车动力传动系实时动力学仿真模型

将动力传动系视为刚体系统,建立适用于开发型驾驶模拟器的动力传动系4自由度实时动力学仿真模型,输入驾驶员的点火开关信号、油门踏板信号、离合器踏板信号及挡位信号,在一定的传动系各部件及驱动轮的运动状态下,传动系模型可向整车动力学模型输出驱动轮上的驱动力矩,从而完成车辆的实时动力学仿真,并进一步向驾驶模拟器输送整车的实时运动状态。仿真与动力性试验的对比结果表明,该模型不但具有实时性,而且可通过整车模型使开发型驾驶模拟器为驾驶员提供逼真的整车运动响应。     

Handling qualities evaluation method based on actual driver characteristics

The present study proposes an objective handling qualities evaluation method using driver-in-the-loop analysis. The driving simulator experiments were performed for various driving conditions, drivers and vehicle dynamics. The response characteristics of the driver model and the closed-loop system were analyzed. The analysis revealed the driving strategies clearly, indicating the importance of closed-loop analysis. Using the identified driver model and its strategies, a cost function of the handling qualities was constructed. The cost function can be used to estimate the handling qualities analytically from the vehicle dynamics. The proposed method was validated by comparison with the handling qualities evaluation rated by the driver's comments.