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.     

Steering disturbance rejection using a physics-based neuromusculoskeletal driver model

The aim of this work is to develop a comprehensive yet practical driver model to be used in studying driver–vehicle interactions. Drivers interact with their vehicle and the road through the steering wheel. This interaction forms a closed-loop coupled human–machine system, which influences the driver's steering feel and control performance. A hierarchical approach is proposed here to capture the complexity of the driver's neuromuscular dynamics and the central nervous system in the coordination of the driver's upper extremity activities, especially in the presence of external disturbance. The proposed motor control framework has three layers: the first (or the path planning) plans a desired vehicle trajectory and the required steering angles to perform the desired trajectory; the second (or the musculoskeletal controller) actuates the musculoskeletal arm to rotate the steering wheel accordingly; and the final layer ensures the precision control and disturbance rejection of the motor control units. The physics-based driver model presented here can also provide insights into vehicle control in relaxed and tensed driving conditions, which are simulated by adjusting the driver model parameters such as cognition delay and muscle co-contraction dynamics.     

Risk management algorithm for rear-side collision avoidance using a combined steering torque overlay and differential braking

This paper describes a risk management algorithm for rear-side collision avoidance. The proposed risk management algorithm consists of a supervisor and a coordinator. The supervisor is designed to monitor collision risks between the subject vehicle and approaching vehicle in the adjacent lane. An appropriate criterion of intervention, which satisfies high acceptance to drivers through the consideration of a realistic traffic, has been determined based on the analysis of the kinematics of the vehicles in longitudinal and lateral directions. In order to assist the driver actively and increase driver's safety, a coordinator is designed to combine lateral control using a steering torque overlay by motor-driven power steering and differential braking by vehicle stability control. In order to prevent the collision while limiting actuator's control inputs and vehicle dynamics to safe values for the assurance of the driver's comfort, the Lyapunov theory and linear matrix inequalities based optimisation methods have been used. The proposed risk management algorithm has been evaluated via simulation using CarSim and MATLAB/Simulink.     

A Research on Analytical Method of Driver-Vehicle-Environment System for Construction of Intelligent Driver Support System

In this study, a driver model with the multiple regression and the neural network is constructed to analyze the relationship between the driver's control action and the information that includes data of vehicle behavior and environment. Using these models, effectiveness of the information to control the action of a driver is examined. To evaluate the intelligent driver support systems, Mental Work Load (MWL) model is constructed with the multiple regression and the neural network. MWL is expressed as Heart Rate Variability (HRV). Measured HRV data and calculated HRV data with MWL model show good agreement. Effectiveness of information is examined using the MWL model. From these results, it is shown that the analytical method with the driver's MWL can be used to assess and improve the intelligent driver support systems as the next stage of this research.     

Vehicle stability control based on driver’s emergency alignment intention recognition

In this work, the reference model modification strategy for vehicle stability control based on driver's intention recognition under emergent obstacle avoidance situation was proposed. First the conflicts between the driver's emergency alignment (EA) intention and vehicle response characteristics were analyzed in critical emergent obstacle avoidance situation. Second combining steering wheel angle and its speed, the driver's EA intention was recognized. The reference model modification strategy based on steering operation index (SOI) was presented. Then a LQR model following controller with tire cornering stiffness adaption was used to generate direct yaw moment for tracking modified reference yaw rate and reference sideslip angle. Finally based on the four-in-wheel-motor-drive (FIWMD) electric vehicles (EV), double lane change and slalom tests were conducted to compare the results using modified reference model with the results using normal reference model. The experimental tests have proved the effectiveness of the reference model modification strategy based on driver's intention recognition.     

A new shared control for lane keeping and road departure prevention

In recent years, the driver's active assistances have become important features in commercialised vehicles. In this paper, we present one of these features which consists of an advanced driver assistance system for lane keeping. A thorough analysis of its performance and stability with respect to variations in driver behaviour will be given. Firstly, the lateral control model based on visual preview is established and the kinematics model based on visual preview, including speed and other factors, is used to calculate the lateral error and direction error. Secondly, and according to the characteristics of the lateral control, an efficient strategy of intelligent electric vehicle lateral mode is proposed. The integration of the vehicle current lateral error and direction error is chosen as the parameter of the sliding mode switching function to design the sliding surface. The control variables are adjusted according to the fuzzy control rules to ensure that they meet the existence and reaching condition. A new fuzzy logic-based switching strategy with an efficient control law is also proposed to ensure a level of continuous and variable sharing according to the state of the driver and the vehicle positioning on the roadway. The proposed control law acts either at the centre of the lane, as a lane keeping assistance system to reduce the driver's workload for long trips, or as a lane departure avoidance system that intervenes for unintended lane departures. Simulation results are included in this paper to explain this concept.     

A passenger car driver model for higher lateral accelerations

Due to increasing demands for time and cost efficient vehicle and driver assistant systems development, numerical simulation of closed-loop manoeuvres becomes increasingly important. Thus, the driver has to be considered in the modelling. On the basis of a two-layer approach to model a driver's steering behaviour, the field of application is extended to higher lateral accelerations in this study. An analytical method to determine the driver parameters is presented, which is based on the two-wheel vehicle model. The simulation results are determined using a full vehicle model including all essential nonlinearities. Standard manoeuvres in the nonlinear range of vehicle handling behaviour are performed. A cornering manoeuvre is chosen to show the characteristics of the proposed driver model.     

Study of breath alcohol detector

A novel detector of breath alcohol for a driver has been developed. The detector has three features. A mouth piece is not required because driver's breath is introduced to the detector by a suction pump. The influence of fluctuations of driver's breath flow is extremely reduced by the calibration of alcohol concentration using a humidity change. The detector is able to measure breath alcohol concentration rapidly, and the measurement time is about 2 to 3 seconds. The excellent performance have been demonstrated both in breath alcohol simulation tests and in a drunken persons test.     

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.     

Modelling of a driver's behaviour considering roll motion

This paper presents an anlysis of the control behaviour of a driver during curves and lane changes. We model the driver's behaviour taking the roll motion of the vehicle into consideration. Using this model with constraints on the roll angle, it is possible to model lane change maneuvers without specifying a path. The validity of the model is investigated through a comparison between computer simulation and experimentation using a driving simulator system.     

Modeling Human Vehicle Driving by Model Predictive Online Optimization

A driver model is designed which relates the driver's action to his perception, driving experience, and preferences over a wide range of possible traffic situations. The basic idea behind the work is that the human uses his sensory perception and his expert knowledge to predict the vehicle's future behavior for the next few seconds (prediction model). At a certain sampling rate the vehicle's future motion is optimized using this prediction model, in order to meet certain objectives. The human tries to follow this optimal behavior using a compensatory controller. Based on this hypothesis, human vehicle driving is modeled by a hierarchical controller. A repetitive nonlinear optimization is employed to plan the vehicle's future motion (trajectory planning task), using an SQP algorithm. This is combined with a PID tracking control to minimize its deviations. The trajectory planning scheme is experimentally verified for undisturbed driving situations employing various objectives, namely ride comfort, lane keeping, and minimized speed variation. The driver model is then applied to study path planning during curve negotiation under various preferences. A highly dynamic avoidance maneuver (standardized ISO double lane change) is then simulated to investigate the overall stability of the closed loop vehicle/driver system.     

A novel direct yaw moment controller for in-wheel motor electric vehicles

A novel direct yaw moment controller is developed in this paper. A hierarchical control architecture is adopted in the controller design. In the upper controller, a driver model and a vehicle model are used to obtain the driver's intention and the vehicle states, respectively. The upper controller determines the desired yaw moment by means of sliding mode control. The lower controller distributes differential longitudinal forces according to the desired yaw moment. A nonlinear tyre model, ‘UniTire’, is utilised to develop the novel distribution strategy and the control boundary.     

Study on behavior of cab-over truck driver looking at a display while driving

This paper discusses how a cab-over truck driver looks at information shown in a display while driving, to give information to the driver safely and securely. The authors investigated a driver's behavior in changing focus to an information display while driving an actual truck and a driving simulator. Relation between distribution of attention and individual lengths of time to change focus to the display was examined. Average diversion time away from road was found to be approximately 1.7 s, longer than that for passenger cars under similar traffic conditions. Driving skills do not have any influence on individual lengths of time.     

An Advanced Steering System with Active Kinesthetic Feedback for Handling Qualities Improvement

SUMMARY

Advanced Steering System with artificial steering wheel torque-active kinesthetic information feedback for improving handling qualities is discussed. Fundamentally the structure of the system may be considered to another form of model following control. In this system, a driver always remains in the control loop and receives steering control information which give him/her a direct hint to steer a steering wheel. This system works as a stability and control augmentation system of the vehicle to improve the vehicle handling qualities both in compensatory and pursuit control task, and is expected to reduce driver's workload. Effects of this system are analyzed in terms of man-machine system characteristics. Identification of driver dynamics was carried out to find why such improvement could be achieved. Availability of the proposed system is verified by analysis, simulator and proving ground tests.     

A novel integrated chassis controller for full drive-by-wire vehicles

In this paper, a systematic design with multiple hierarchical layers is adopted in the integrated chassis controller for full drive-by-wire vehicles. A reference model and the optimal preview acceleration driver model are utilised in the driver control layer to describe and realise the driver's anticipation of the vehicle's handling characteristics, respectively. Both the sliding mode control and terminal sliding mode control techniques are employed in the vehicle motion control (MC) layer to determine the MC efforts such that better tracking performance can be attained. In the tyre force allocation layer, a polygonal simplification method is proposed to deal with the constraints of the tyre adhesive limits efficiently and effectively, whereby the load transfer due to both roll and pitch is also taken into account which directly affects the constraints. By calculating the motor torque and steering angle of each wheel in the executive layer, the total workload of four wheels is minimised during normal driving, whereas the MC efforts are maximised in extreme handling conditions. The proposed controller is validated through simulation to improve vehicle stability and handling performance in both open- and closed-loop manoeuvres.     

Physiological evaluation of effect on driver's mental work load for a newly-developed semi-automatic transmission system

A qualitative evaluation was made on how a newly-developed semi-automatic transmission system “ESCOT” (Easy, Safe Controlled Transmission) reduced the mental work load on drivers, by measuring their physiological reactions. Heart rate, brain waves (β-waves), and galvanic skin response clearly changed under driving conditions that increased the driver's mental work load, such as when shifting gears, braking and turning at a crossing. From these measurements, a mental work load index was empirically derived. The average mental work load index for long-distance driving was reduced by about 30% when driving a tractor equipped with the ESCOT system compared to the conventional 12-speed manual transmission.     

Influencing driver chosen cornering speed by means of modified steering feel

This paper presents an investigation about influencing the driver's behaviour intuitively by means of modified steering feel. For a rollover indication through haptic feedback a model was developed and tested that returned a warning to the driver about too high vehicle speed. This was realised by modifying the experienced steering wheel torque as a function of the lateral acceleration. The hypothesis for this work was that drivers of heavy vehicles will perform with more margin of safety to the rollover threshold if the steering feel is altered by means of decreased or additionally increased steering wheel torque at high lateral acceleration. Therefore, the model was implemented in a test truck with active steering with torque overlay and used for a track test. Thirty-three drivers took part in the investigation that showed, depending on the parameter setting, a significant decrease of lateral acceleration while cornering.     

Application of time-variant predictive control to modelling driver steering skill

The paper addresses the need for improved mathematical models of human steering control. A multiple-model structure for a driver's internal model of a nonlinear vehicle is proposed. The multiple-model structure potentially offers a straightforward way to represent a range of driver expertise. The internal model is combined with a model predictive steering controller. The controller generates a steering command through the minimisation of a cost function involving vehicle path error. A study of the controller performance during an aggressive, nonlinear steering manoeuvre is provided. Analysis of the controller performance reveals a reduction in the closed-loop controller bandwidth with increasing tyre saturation and fixed controller gains. A parameter study demonstrates that increasing the multiple-model density, increasing the weights on the path error, and increasing the controller knowledge range all improved the path following accuracy of the controller.     

Optimization of Vehicles Elasto-Damping Elements Characteristics from the Aspect of Ride Comfort

SUMMARY

Spatial random vibrations of a vehicle that arise during driving represent an important factor in functioning of a dynamic system: Driver - Vehicle - Environment. They carry certain information for driver and also cause fatigue of driver and passenger.

This is the reason why the tendency is towards the minimization of vibratory loads, what in practice can be achieved by optimization of characteristics of elasto - damping elements of a vehicle.

In this paper for optimization of elasto - damping elements of a vehicle we used a complex nonlinear model of a driver and a vehicle during the straight - line motion of the vehicle on a rough road. Optimization was performed by application of the Hooke - Jeeves method and by use of outside penalty functions as well as the objective function that enabled simultaneous optimization of vertical vibrations of the driver's seat, vibrations of the steering wheel, and normal reactions in the contact surface of the tyre and road. The optimization was performed with application of the computer HP 9000/800 SE on the example of a medium passenger car.     

基于Logistic回归的驾驶人跟车危险认知模型

为探索紧急情况下驾驶人跟车危险认知机理,利用图像式汽车行驶记录仪在中国首次开展了大规模的交通冲突调查,基于采集的实际交通环境下的追尾冲突数据,分析了驾驶人制动操作与驾驶人安全和危险状态判断之间的关系,提出了基于Logistic回归的驾驶人跟车危险认知模型。研究结果表明:车头时距和碰撞时间是影响驾驶人跟车危险判断的重要参数;经模型的拟合优度和预测准确性检验,提出的模型在城市交通环境下具有较好的适用性。