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.     

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.     

eTS fuzzy driver model for simultaneous longitudinal and lateral vehicle control

In this paper, evolving Takagi-Sugeno (eTS) fuzzy driver model is proposed for simultaneous lateral and longitudinal control of a vehicle in a test track closed to traffic. The developed eTS fuzzy driver model can capture human operator’s driving expertise for generating desired steering angle, throttle angle and brake pedal command values by processing only information which can be supplied by the vehicle’s on-board control systems in real time. Apart from other fuzzy rule based (FRB) models requiring human expert knowledge or off-line clustering, the developed eTS driver model can adapt itself automatically, even ‘from scratch’, by an on-line learning process using eTS algorithm while human driver is supervising the vehicle. Proposed eTS fuzzy driver model’s on-line human driver identification capability and autonomous vehicle driving performance were evaluated on real road profiles created by digitizing two different intercity express ways of Turkey in IPG© CarMaker® software. The training and validation simulation results demonstrated that eTS fuzzy driver model can be used in product development phase to speed up different tests via realistic simulations. Furthermore eTS fuzzy driver model has an application potential in the field of autonomous driving.     

Development of an errorable car-following driver model

An errorable car-following driver model is presented in this paper. An errorable driver model is one that emulates human driver’s functions and can generate both nominal (error-free), as well as devious (with error) behaviours. This model was developed for evaluation and design of active safety systems. The car-following data used for developing and validating the model were obtained from a large-scale naturalistic driving database. The stochastic car-following behaviour was first analysed and modelled as a random process. Three error-inducing behaviours were then introduced. First, human perceptual limitation was studied and implemented. Distraction due to non-driving tasks was then identified based on the statistical analysis of the driving data. Finally, time delay of human drivers was estimated through a recursive least-square identification process. By including these three error-inducing behaviours, rear-end collisions with the lead vehicle could occur. The simulated crash rate was found to be similar but somewhat higher than that reported in traffic statistics.     

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.     

Driver models in automobile dynamics application

Understanding the driver of an automobile has been attractive to researchers from many different disciplines for more than half a century. On the basis of their acquirements, models of the (human) driver have been developed to better understand, analyse and improve the combined couple of driver and automobile. Due to distinctive demands on the models in accordance with different kinds of applications, a variety of driver models is available. An overview of driver models is given with respect to their application and different methodical modelling approaches. The emphasis is put on the interest of engineers, who generally focus on the automobile (like design and optimization of vehicle components and the overall vehicle dynamics behaviour) by applying their approved (mathematical) methods. Nonetheless, a brief look beyond is added to better complete the view on the involved task of driving and driver modelling for automobile dynamics application.     

Vehicle sensor data-based analysis on the driving style differences between operating indoor simulator and on-road instrumented vehicle

Abstract

Indoor simulator and on-road instrumented vehicle are the most popular ways to analyze driving behaviors by using collected Vehicle Sensor Data (VSD). However, for a same driver, the driving performance could be different in the real world and in the simulated world. Even though many studies have been conducted to discover the differences of driving behaviors in these two circumstances, little research has focused on analyzing the differences in driving style, which can provide more integrated knowledge of a driver from the natural structure, stimulus–response mechanism, of driving behaviors. Therefore, in this paper, the driving styles in both the real world and the simulated world are extracted by implementing the nonnegative matrix factorization method on the collected VSD data. Through this analysis, the driving style differences can be quantitatively described and discussed in detail. It is found that the drivers tend to be more unstable and sometimes aggressive when driving the simulator and the deviation in the perception of temporal gap in two circumstances is also discovered. The research findings are particularly valuable to calibrate the driving simulator and construct more reliable driving behavior models.     

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.     

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.     

Comparison of All-Wheel Steerings in the System Driver-Vehicle

Different load or tires and a drive on an ice-coated road can overcharge a driver to such an extend, that the result may be an accident. Therefore the aim of development is a self-acting compensation of the vehicle to different vehicle transfer behaviour (invariant vehicle behaviour).

The calculation of so called optimal characteristics shows, that only rear-wheel steering cannot realize this aim of development. Therefore an additional front-wheel angle, which is not influenced by the driver, is necessary. A transfer function can be calculated in order to get controlled steering of the rear wheels without the influence of load.

It is not possible to realize optimal characteristics, because the parameters of the vehicle are difficult to measure. Only an optimal diagnosis and control of driving condition realize a relief for the driver in every driving situation in order to avoid most of the accidents.

The often demanded sideslip angle compensation only worsens driving conditions on ice-coated roads. Therefore systems which identify the driving condition themselves have to be favoured in any case.     

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。     

Driver-Vehicle-Lateraldynamics under Regular Driving Conditions

In order to minimize accidents and achieve comfortable handling it is necessary to analyse the control behaviour of the driver-vehicle system and adapt the vehicle to the driver. However, most of the tests have shown, that under normal driving conditions the driver adapts himself to the vehicle, which is exactly the other way round as it was originally thought. In this paper it would be shown, how the driver adapts himself to the vehicle and which technical parameters may effect this adaptation.     

Comparison of All-Wheel Steerings in the System Driver-Vehicle

SUMMARY

Different load or tires and a drive on an ice-coated road can overcharge a driver to such an extend, that the result may be an accident. Therefore the aim of development is a self-acting compensation of the vehicle to different vehicle transfer behaviour (invariant vehicle behaviour).

The calculation of so called optimal characteristics shows, that only rear-wheel steering cannot realize this aim of development. Therefore an additional front-wheel angle, which is not influenced by the driver, is necessary. A transfer function can be calculated in order to get controlled steering of the rear wheels without the influence of load.

It is not possible to realize optimal characteristics, because the parameters of the vehicle are difficult to measure. Only an optimal diagnosis and control of driving condition realize a relief for the driver in every driving situation in order to avoid most of the accidents.

The often demanded sideslip angle compensation only worsens driving conditions on ice-coated roads. Therefore systems which identify the driving condition themselves have to be favoured in any case.     

The perspectives of research for enhancing active safety based on advanced control technology

This paper considers the scope and the methodologies for enhancing active safety of road vehicles by sensing and control technologies. The first part of this paper introduces statistical data of traffic accidents in Japan, and describes the development of the drive recorder for accident/incident survey and analysis. Based on vehicle dynamics data, the algorithm of the drive recorder for capturing near-miss incident data is introduced. The second part of this paper reviews control problems of vehicle dynamics on micro-scale electric vehicles for enhancing vehicle dynamics and driving assistance function. In particular, the direct yaw moment control using in-wheel-motors and the active front steering control algorithm are described. The third part of the paper introduces the advanced driver assistance system adapted to driver characteristics and traffic situations. This part mainly describes an adaptive system, which adjusts the assisting manoeuvre depending on individual driver behaviour and situation, and some experimental investigations using the active interface vehicle and driving simulator. Finally, some perspectives and new challenges for future research on vehicle control technology are mentioned.     

Driver-Vehicle-Lateraldynamics under Regular Driving Conditions

SUMMARY

In order to minimize accidents and achieve comfortable handling it is necessary to analyse the control behaviour of the driver-vehicle system and adapt the vehicle to the driver. However, most of the tests have shown, that under normal driving conditions the driver adapts himself to the vehicle, which is exactly the other way round as it was originally thought. In this paper it would be shown, how the driver adapts himself to the vehicle and which technical parameters may effect this adaptation.     

Driver steering and muscle activity during a lane-change manoeuvre

The article reports an experimental study of driver steering control behaviour in a lane-change manoeuvre. Eight test subjects were instrumented with electromyography to measure muscle activation and co-contraction. Each subject completed 30 lane-change manoeuvres with one vehicle on a fixed-base driving simulator. For each driver, the steering torque feedback characteristic was changed after every ten manoeuvres; the response of the vehicle to steering angle inputs was not changed. Drivers' control strategies were found to be robust to changes in steering torque feedback. Path-following errors, muscle activity and muscle co-contraction all reduce with the number of lane-changes performed by the driver, suggesting the existence of a learning process. Comparing the test subjects, there was some evidence that high levels of co-contraction were used to allow high-frequency steering inputs to be generated. The results contribute to the understanding of vehicle–driver (and more generally, human–machine) dynamic interaction.     

邻车切入工况下前撞预警系统的驾驶人依赖特性

为提升邻车切入工况下的行车安全,基于驾驶模拟实验平台,研究了驾驶人对前撞预警系统的依赖特性评价方法以改进预警系统的设计。以预警时机(即碰时间TTC)为研究变量,采集了12名驾驶人的实验数据,以制动依赖指数、次任务评分为2项客观指标,以危险度评分、信任度评分为2项主观指标,建立了评价体系模型,实现了对驾驶人系统依赖程度的量化评价。设计了L9(34)正交实验,建立了依赖特性评价回归模型。结果表明:预警时机(TTC)对依赖特性的影响最为显著:过晚的预警时机(TTC=2.4 s)降低系统的有效性;过早的预警时机(TTC=1.2 s)易导致驾驶人对系统过度依赖。因而,适度推迟预警时机(TTC=1.8 s)可以抑制依赖性的产生,提升系统的安全性。     

Dynamic stability of forklift trucks in cornering situations: parametrical analysis using a driving simulator

This paper describes the examination of the vehicle dynamics and stability of four-wheeled forklift trucks (FLTs) in cornering situations. Cornering at excessive speed is one major reason for fatal accidents with forklifts caused by lateral tipover. In order to increase the lateral stability of this kind of working machinery, the influence of certain important design properties has been studied using an appropriate vehicle simulation model and a driving simulator. The simulation model is based on a multi-body system approach and includes submodels for the propulsion system and the tyres. The driving behaviour of the operator has not been modelled. Instead, a driving simulator has been built up and a real human driver was employed for ensuring adequate and realistic model input. As there have not been any suitable standardised test manoeuvres available for FLTs, a new driving test has been developed to assess the lateral stability. This test resembles the well-known J turn/Fishhook turn, but includes a more dynamic counter-steering action. Furthermore, the dimensions of the test track are defined. Therefore, the test is better adapted to the driving dynamics of forklifts and reflects the real driver behaviour more closely. Finally, a parametrical study has been performed, examining the influence of certain important technical properties of the truck such as the maximum speed, the position of centre of gravity, rear axle design features and tyre properties. The results of this study may lead to a better understanding of the vehicle dynamics of forklifts and facilitate goal-oriented design improvements.     

Highly reliable driving workload analysis using driver electroencephalogram (EEG) activities during driving

Traffic accidents are caused by various factors, which can be classified into human factors, vehicle factors and environmental factors. Recently, human factors have been drawing particular attention as efforts are being made to enhance the safety performance of vehicles and improve road conditions. Driving distraction caused by an increased driving workload is a representative human factor. Various studies in the past have attempted to quantify the driving workload by using EEG activities. However, they have failed to consider vibration properties generated from vehicle engines. A number of noise signals were included in brainwave signal processing, which resulted in a failure to obtain reliable outcomes. Thus, this study suggests driver EEG activities free of vehicle engine secondary vibration in order to develop a method that analyzes the driving workload with high statistical reliability. By using the analytical method developed in this study, standard values of driving workload for straight and left-turn driving that has statistical significance could be calculated. The analytical method for driving workload created by this study can be applied to HVI and road design.     

Psychological perspectives on changing driver attitude and behaviour

From a psychological viewpoint, several behaviour change techniques may be used to change unsate driver behaviour, e.g. informing, persuading rewarding, punishing etc. The motivation underlying the behaviour determines to a large degree how successful these behaviour change strategies may be. In the article, three broad classes of explanation for driver behaviour are distinguished: reasoned or planned behaviour, impulsive or emotional behaviour and habitual behaviour. Reasoned behaviour is under voliitional control of the driven and is determined by intention to commit the behaviour, which is in turn dependent on attitudes and subjective norms. Emotional behaviour may take range of expressive forms, varying from impulsive reactions to unexpected traffic situations to aggressive driving behaviours elicited by perceived norm-violations of other road users. Habitual behaviour occurs mindlessly, without forethought or conscious information-processing. The transfer of objective knowledge, the cornerstone of persuasive road safety campaigns and road safety education, may be useful in itself in changing reasoned driver behaviour, but may be amiss in changing unplanned, emotionally or habitually driven behaviours. For addressing these types of behaviour, other or additional strategies are considered.