Snowplow Steering Guidance with Gain Stabilization

Acquisition and utilization of lateral guidance information is crucial for steering a vehicle. With practice, drivers can successfully perform the steering function using visual perception and hand-eye coordination. However, this seemingly simple task becomes difficult when the visual information loses its clarity. Driving a snowplow during whiteout conditions is one such example. In order to improve the safety and efficiency of snow removal operations, a supplemental guidance display was proposed and successfully implemented in a California Department of Transportation (Caltrans) snowplow. The guidance information was calculated based on the magnetic markers embedded in the roadway. The crucial step to this success is a transformation of the guidance display problem into a robust driver-in-the-loop control problem. This transformation considers the ‘display’ law as part of the overall driver-steering-control algorithm. Two key ‘assumptions’ for this design are (1) the ‘display’ law should be designed in such a way that drivers can use ‘proportional’ control gain alone to satisfy the performance and stability requirements of the steering tasks, and (2) the driver steering model can be described as a combination of gain and dynamic delay under the display law in (1). This paper describes the validation process for the ‘gain’ stabilization design concept as well as the associated driver steering model using the initial snowplow test data.     

A Snowplow Steering Guidance System Using Roadway Markers - Problem Formulation and Solution Concept

Acquisition and utilization of lateral guidance information is crucial for steering a vehicle. With practice, human drivers have performed this function quite successfully using visual perception and hand -eye coordination. However, this seemingly simple task becomes difficult when the visual information loses its clarity. Driving a snowplow during whiteout conditions is one such example. In order to improve the safety and efficiency of the snow removal operation, a supplemental guidance display was proposed and instrumented in a Caltrans (California Department of Transportation) snowplow. The guidance information was calculated based on the magnetic markers embedded in the roadway. Operations for the steering guidance system were successfully conducted on a 6.25 km freeway over Donner Summit on Interstate 80 in California during the 1998-1999 winter season. The key to this success was a transformation of the guidance display problem into a control problem. This paper describes the problem formulation and the solution concept for this steering guidance system.     

A Snowplow Steering Guidance System Using Roadway Markers - Problem Formulation and Solution Concept

Acquisition and utilization of lateral guidance information is crucial for steering a vehicle. With practice, human drivers have performed this function quite successfully using visual perception and hand -eye coordination. However, this seemingly simple task becomes difficult when the visual information loses its clarity. Driving a snowplow during whiteout conditions is one such example. In order to improve the safety and efficiency of the snow removal operation, a supplemental guidance display was proposed and instrumented in a Caltrans (California Department of Transportation) snowplow. The guidance information was calculated based on the magnetic markers embedded in the roadway. Operations for the steering guidance system were successfully conducted on a 6.25 km freeway over Donner Summit on Interstate 80 in California during the 1998–1999 winter season. The key to this success was a transformation of the guidance display problem into a control problem. This paper describes the problem formulation and the solution concept for this steering guidance system.     

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.     

Research on interactive steering control strategy between driver and AFS in different game equilibrium strategies and information patterns

In the past decade, several publications have shown that it is advisable to design an advanced driver assistance system using a shared control structure. This paper is concerned with the modelling and verification of an interactive steering control strategy between a driver and an active front steering (AFS) controller to investigate the complex interactions between human driver and an AFS system. Using game theory as a general framework, a more comprehensive mathematical model system of interactive steering control potentially applicable to explore human drivers’ behaviours in shared control of intelligent vehicles is presented and discussed in this paper. The effects of different information patterns, namely the open-loop pattern and the closed-loop feedback pattern on modelling shared steering control between driver and AFS have been investigated. Simulation and hardware-in-loop implementation results prove the validity of steering interactive modelling in different game information patterns. Specifically, the results show that, in the Nash equilibrium strategy situation, the driver and the AFS controller may become more rational and reasonable in the process of completing the same dynamic task in the closed-loop feedback information patterns compared to the open-loop ones; and the differences between feedback Nash and feedback Stackelberg may depend on the step size of discretisation.     

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.     

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.     

Links between subjective assessments and objective metrics for steering

The characteristics of steering perception are decisive factors for overall driver preference and for vehicle safety. Car manufacturers are continuously required to tune the characteristics of the vehicle and have a strong need to be more effective in the design and evaluation of cars. Using only objective metrics (OM) can result in unwanted steering feel and using only subjective assessments (SA) is time-consuming, costly and non-repetitive. Before a tool can be built to predict the steering feel in front-end development and to improve design knowledge from the full vehicle level to the component level, links between subjective assessments and objective metrics must be found and analysed. The data collected for the study presented in this paper include subjective ratings from expert drivers and objective measurements made with steering robots, involving twelve expert drivers and over twenty vehicles across four different vehicle classes. Linear regression and neural network analysis (NN) have been used to explore reliable subjective-objective links. The tools and methods used in this research showed promising results. Most of the links found concern response and torque feedback. The preferred ranges of some crucial objective metrics leading to more desirable steering feel have been defined and presented. The results indicate that it would be possible for car manufacturers to develop new vehicles more effectively with a steering feel in line with the design criteria by using the tools and methods investigated in this paper.     

Nonlinear PI front and rear steering control in four wheel steering vehicles

Vehicle steering dynamics show resonances, which depend on the longitudinal speed, unstable equilibrium points and limited stability regions depending on the constant steering wheel angle, longitudinal speed and car parameters.

The main contribution of this paper is to show that a combined decentralized proportional active front steering control and proportional-integral active rear steering control from the yaw rate tracking error can assign the eigenvalues of the linearised single track steering dynamics, without lateral speed measurements, using a standard single track car model with nonlinear tire characteristics and a non-linear first-order reference model for the yaw rate dynamics driven by the driver steering wheel input. By choosing a suitable nonlinear reference model it is shown that the responses to driver step inputs tend to zero (or reduced) lateral speed for any value of longitudinal speed: in this case the resulting controlled vehicle static gain from driver input to yaw rate differs from the uncontrolled one at higher speed. The closed loop system shows the advantages of both active front and rear steering control: higher controllability, enlarged bandwidth for the yaw rate dynamics, suppressed resonances, new stable cornering manoeuvres, enlarged stability regions, reduced lateral speed and improved manoeuvrability; in addition comfort is improved since the phase lag between lateral acceleration and yaw rate is reduced.

For the designed control law a robustness analysis is presented with respect to system failures, driver step inputs and critical car parameters such as mass, moment of inertia and front and rear cornering stiffness coefficients. Several simulations are carried out on a higher order experimentally validated nonlinear dynamical model to confirm the analysis and to explore the robustness with respect to unmodelled dynamics.     

Nonlinear PI front and rear steering control in four wheel steering vehicles

Vehicle steering dynamics show resonances, which depend on the longitudinal speed, unstable equilibrium points and limited stability regions depending on the constant steering wheel angle, longitudinal speed and car parameters.

The main contribution of this paper is to show that a combined decentralized proportional active front steering control and proportional-integral active rear steering control from the yaw rate tracking error can assign the eigenvalues of the linearised single track steering dynamics, without lateral speed measurements, using a standard single track car model with nonlinear tire characteristics and a non-linear first-order reference model for the yaw rate dynamics driven by the driver steering wheel input. By choosing a suitable nonlinear reference model it is shown that the responses to driver step inputs tend to zero (or reduced) lateral speed for any value of longitudinal speed: in this case the resulting controlled vehicle static gain from driver input to yaw rate differs from the uncontrolled one at higher speed. The closed loop system shows the advantages of both active front and rear steering control: higher controllability, enlarged bandwidth for the yaw rate dynamics, suppressed resonances, new stable cornering manoeuvres, enlarged stability regions, reduced lateral speed and improved manoeuvrability; in addition comfort is improved since the phase lag between lateral acceleration and yaw rate is reduced.

For the designed control law a robustness analysis is presented with respect to system failures, driver step inputs and critical car parameters such as mass, moment of inertia and front and rear cornering stiffness coefficients. Several simulations are carried out on a higher order experimentally validated nonlinear dynamical model to confirm the analysis and to explore the robustness with respect to unmodelled dynamics.     

Automotive steering system preferences evaluated using a driving simulator

The automotive steering system is the primary channel through which road and vehicle behavior feedback is transmitted to the driver. While the driver provides directional platform control through the steering wheel, perceptions of the vehicle’s handling responsiveness are simultaneously transmitted back to the driver allowing for correction of any instabilities the vehicle may encounter. Based on these factors, drivers often pay special attention to the steering system when deciding what vehicle to purchase. Therefore, a significant amount of effort and time is invested in attempting to determine the optimal design of steering system components and configurations. In this study, the determination of an optimal steering configuration was attempted based on responses obtained from questionnaires that subjects answered. The questions were designed to evaluate the degree of satisfaction regarding the “control”, “ease of operation”, and “fun” participants experienced after each driving run. During the study, human subjects drove a driving simulator for 15 combinations of 3 different roadway environments and 5 different steering configurations, filling out a questionnaire after each scenario. The subjects were also classified as a type of driver (“utility”, “enthusiast”, and/or “performance”). The study attempted to determine if the mean values of questionnaire responses for “control”, “ease”, and “fun” type of questions changed as the scenario and/or driver type changed. Analysis of Variance (ANOVA) was used to determine if the mean values of the three types of questions were statistically different. The overall results suggest that the average responses for vehicle “control”, “ease”, and the “fun” type of questions were dependent on the type of roadway environment; however, only the responses for “fun” type of questions were influenced by the given steering configurations. Indeed, the steering system can impact the driver’s perceptions of the vehicle’s operational experience.     

From robotics to automotive: Lane-keeping and collision avoidance based on elastic bands

In the near future, drivers will more and more share vehicle guidance with assistance systems. This contribution provides a potential field-based approach to the underlying motion planning problem. In doing so, the concept of elastic bands, known from robotics, is extended to automotive applications. Contrary to robotic applications, extrapolation routines anticipating the motion of the surrounding traffic are incorporated in the motion planning. New in this paper is the distinction of different types of obstacles such as traffic staying in its lane and traffic intending to depart from it. Beyond that, the motion planning adapts to the driver's commands. The driver can be included in the overall control loop by means of a haptic interface generating a torque that depends on the difference of the actual steering angle and the steering angle necessary to follow the planned trajectory. However, this contribution focuses only on the underlying motion planning procedure.     

停车诱导信息集成结构模型设计及实现

面对动态、多变、实时的交通信息以及驾驶员对停车场信息的需求,停车管理需要走向智能化。停车诱导系统能为驾驶员提供实时的停车场信息以及周边道路情况,在基于多Agent系统的基础上进行分析,给出停车诱导信息集成结构模型,并利用面向对象的Agent进行设计,最后指出实现该系统还需进行的后续研究。     

Development of a Driving Behavior-Based Collision Warning System Using a Neural Network

An advanced driver assistance system (ADAS) uses radar, visual information, and laser sensors to calculate variables representing driving conditions, such as time-to-collision (TTC) and time headway (THW), and to determine collision risk using empirically set thresholds. However, the empirically set threshold can generate differences in performance that are detected by the driver. It is appropriate to quickly relay collision risk to drivers whose response speed to dangerous situations is relatively slow and who drive defensively. However, for drivers whose response speed is relatively fast and who drive actively, it may be better not to provide a warning if they are aware of the collision risk in advance, because giving collision warnings too frequently can lower the reliability of the warnings and cause dissatisfaction in the driver, or promote disregard. To solve this problem, this study proposes a collision warning system (CWS) based on an individual driver’s driving behavior. In particular, a driver behavior model was created using an artificial neural network learning algorithm so that the collision risk could be determined according to the driving characteristics of the driver. Finally, the driver behavior model was learned using actual vehicle driving data and the applicability of the proposed CWS was verified through simulation.     

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.     

On-road assessment of in-vehicle driving workload for older drivers: Design guidelines for intelligent vehicles

There has been recent interest in intelligent vehicle technologies, such as advanced driver assistance systems (ADASs) or in-vehicle information systems (IVISs), that offer a significant enhancement of safety and convenience to drivers and passengers. However, the use of ADAS- and IVIS-based information devices may increase driver distraction and workload, which in turn can increase the chance of traffic accidents. The number of traffic accidents involving older drivers that are due to distraction, misjudgment, and delayed detection of danger, all of which are related to the drivers’ declining physical and cognitive capabilities, has increased. Because the death rate in traffic accidents is higher when older drivers are involved, finding ways to reduce the distraction and workload of older drivers is important. This paper generalizes driver information device operations and assesses the workload while driving by means of experiments involving 40 drivers in real cars under actual road conditions. Five driving tasks (manual only, manual primarily, visual only, visual primarily, and visual-manual) and three age groups (younger (20–29 years of age), middle-aged (40–49 years of age), and older (60–69 years of age)) were considered in investigating the effect of age-related workload difference. Data were collected from 40 drivers who drove in a real car under actual road conditions. The experimental results showed that age influences driver workload while performing in-vehicle tasks.     

公路隧道出入口驾驶员视觉负荷评价与建模

公路隧道出入口段行车时驾驶员的视觉负荷变化较大，为进一步量化该路段视觉负荷的变化规律，选取10名驾驶员在秦岭终南山公路隧道柞水—西安段进行实驾试验，采集驾驶员在昼间晴朗天气下通过隧道出入口路段的照度、车速、瞳孔面积等数据。通过分析驾驶员在隧道出入口段的瞳孔面积变化特征，参照峰均比(PAR)、频段比(LF/HF)等物理学、医学参数，提出“瞳孔面积相对变化强度”(RCPA)的概念，将其作为视觉负荷的评价指标，划分出该指标取值与视觉舒适度的关系，并建立RCPA与速度、照度的数学模型。结果表明： 1）RCPA可以较好地体现出隧道出入口段视觉负荷渐变累积和急剧震荡的规律； 2）在即将驶入洞口和刚刚驶离洞口时的瞬时视觉负荷最大，超过了生理极限； 3）RCPA与速度、照度的定量关系可为隧道出入口制定安全速度阈值、改善照明环境提供参考依据。     

基于增益模糊滑模变结构的线控液压转向控制

在分析全液压转向结构与转向偏差机理的基础上,设计了一种线控液压转向系统以实现车辆转向同步,消除转向偏差;针对现有方法确定的期望转向曲线可跟踪性差而无法实现转向同步,提出一种基于转向效率的期望转向曲线及其可行域确定方法,以最大、最小转向效率对应转向曲线为期望转向曲线可行域的上、下边界,确保期望转向曲线的可跟踪性;针对系统扰动不确定性及油液泄漏非线性,基于组合趋近律滑模控制,并引入饱和函数代替符号函数,在一定程度上抑制了控制系统的抖振;由于组合趋近律增益自适应性不足,导致车轮转角及角速度发生变化时,存在系统动态响应能力差的问题,通过分析车轮转角、角速度与趋近律增益的关系,制定了基于车轮转角及角速度的模糊规则表以自适应调整趋近律增益,实现增益模糊滑模控制,进一步提高油液补偿自适应能力和线控液压转向系统的鲁棒性;最后基于MATLAB/Simulink进行了仿真和试验验证。结果表明：提出的基于转向效率的期望转向曲线均具有良好的可跟踪性能;增益模糊滑模变结构控制具有良好的动态响应特性及控制精度,可有效地消除转向偏差,实现线控液压转向系统的同步转向。     

Modeling and verification of heavy-duty truck drivers’ car-following characteristics

In order to capture drivers’ car-following characteristics and apply this information to the design of an Adaptive Cruise Control algorithm, this paper builds a driver car-following model with vehicle speed-dependent control gains. Proposed for use with heavy-duty truck drivers, we introduced the concept of driver sensitivity to tracking errors, identified driver’s sensitivity to tracking errors and analyzed quantitatively the relationship between control gain and vehicle speed. To model the driver characteristics precisely and concisely, a SVE/SDE (Sensitivity to Velocity Error/Sensitivity to Distance Error) based linear car-following model was built and a nonlinear optimization algorithm was adopted to identify the model parameters. When validating the model accurancy, we proposed a comparative verification method based on hypothesis-testing theory here to reduce the influence of randomicity in the drivers’ manipulation. The modeling and verification indicate that the proposed car-following model is superior to the tranditional linear car-following model, but its structure still approximates linear, which implies it is applicable for the design of a vehicular following controller.     

驾驶风格对驾驶行为的影响

为了揭示驾驶风格对驾驶行为的影响规律,进而提取表征驾驶风格的特征参数,对不同风格驾驶人在感知层和操作层的驾驶行为数据进行了量化分析。首先,基于驾驶行为问卷对18名中国非职业驾驶人进行了驾驶风格问卷调查,并采用主成分分析、K-均值聚类等方法将被试驾驶人分为谨慎型、正常型和激进型3种类型。接着,被试驾驶人在搭载了SmartEye眼动仪的驾驶模拟器上开展了高速公路行车环境下的驾驶试验,同步采集了感知层的视觉特性参数和操作层的驾驶绩效参数,并采用判断抽样的方式将驾驶样本按照驾驶风格和驾驶模式（换道意图和车道保持）进行了划分,共选取了810组有效样本。最后,采用方差分析法分析了不同风格驾驶人在不同驾驶模式下的注视行为、扫视行为、横向控制特性、纵向控制特性方面相关参数的差异显著性,并提取了不同风格间存在显著差异的参数作为表征驾驶风格的特征参数。研究结果表明：驾驶风格越激进,驾驶人对周围环境关注越少,对车辆的横向控制稳定性越差,急加速和急减速行为发生的频次越高;不同风格驾驶人在意图时窗内对后视镜的注视次数（p=0.002）、方向盘转角熵值（p=0.04）、加速踏板开度（p=0.01）、制动踏板开度（p=0.02）这4个参数的差异均较为显著,因此可作为表征驾驶风格的特征参数。