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.     

Lane-keeping benefits of practical rear axle steer

The classic two-degree-of-freedom yaw-plane or ‘bicycle’ vehicle model is augmented with two additional states to describe lane-keeping behaviour and further augmented with an additional control input to steer the rear axle. A simple driver model is hypothesised where the driver closes a loop on a projected lateral lane position. The driver can select the preview distance to compensate driver/vehicle dynamics, consistent with the ‘cross-over’ model found in the literature. A rear axle steer control law is found to be a function of the front axle steering input and vehicle speed that exhibits stability similar to a positive-real system, while at the same time improving the ability of the driver/vehicle system to track a complex curved lane and improving steady-state manoeuvrability. The theoretically derived control law bears similarity to practical embodiments allowing a deeper understanding of the functional value of steering a rear axle.     

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.     

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.     

A closed-loop dynamic simulation-based design method for articulated heavy vehicles with active trailer steering systems

This paper presents a closed-loop dynamic simulation-based design method for articulated heavy vehicles (AHVs) with active trailer steering (ATS) systems. AHVs have poor manoeuvrability at low speeds and exhibit low lateral stability at high speeds. From the design point of view, there exists a trade-off relationship between AHVs’ manoeuvrability and stability. For example, fewer articulation points and longer wheelbases will improve high-speed lateral stability, but they will degrade low-speed manoeuvrability. To tackle this conflicting design problem, a systematic method is proposed for the design of AHVs with ATS systems. In order to evaluate vehicle performance measures under a well-defined testing manoeuvre, a driver model is introduced and it ‘drivers’ the vehicle model to follow a prescribed route at a given speed. Considering the interactions between the mechanical trailer and the ATS system, the proposed design method simultaneously optimises the active design variables of the controllers and passive design variables of the trailer in a single design loop (SDL). Through the design optimisation of an ATS system for an AHV with a truck and a drawbar trailer combination, this SDL method is compared against a published two design loop method. The benchmark investigation shows that the former can determine better trade-off design solutions than those derived by the latter. This SDL method provides an effective approach to automatically implement the design synthesis of AHVs with ATS systems.     

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.     

Experimental approach to developing human driver models considering driver’s human factors

A new approach to develop human driver models (HDMs) is proposed in accordance with the drivers’ generic human factors, i.e., gender, age, and experience, to develop more realistic vehicle simulations. The HDMs consist of three independent and stepwise models with functioning driver’s information processing stages based on the human factors: constructing drivers’ preview distance (PVD) models as a ‘cognition process’, implementing a finite preview optimal control algorithm as a ‘decision process’, and differentiating an ‘operation process’ according to neuromuscular efficiency. Eight different groups of 65 drivers with a 2 × 2 × 2 within-subject design participated in both the PVD estimates and neuromuscular efficiency tests to develop a set of statistically different HDMs. Regarding the preview distance models, an analysis of covariance (ANCOVA) procedure was adopted with two covariates (i.e., vehicle velocity and road curvature), while analyses of variance (ANOVAs) were performed on the neuromuscular efficiency parameters. The ANCOVA procedure produced eight significantly different cognition processes, whereas the ANOVAs revealed gender differences for the drivers’ neuromuscular systems. Moreover, an integrated vehicle simulation was configured with the HDMs using Carsim and Simulink software to observe the differential effects of both the cognition and operation processes on a double-lane-change (DLC) maneuver. During the simulations, gender differences in real-world DLC tests were also identified, especially between the male-oldexpert and the female-young-novice HDMs. The results presented in this study suggest that differentiating HDMs according to human factors is an essential process when utilizing vehicle simulations in the early stage of developing an intelligent vehicle 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.     

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.     

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.     

汽车电动助力转向系统及发展

传统的液压助力转向系统只能提供有效的转向助力,但不能根本地解决汽车驾驶员操纵＂路感＂不足的问题。文章介绍了汽车电动助力转向系统（EPS）的构成、分类、主要特点及未来发展方向。说明了EPS不但可以提供轻便和灵敏的操作,而且可以提供良好的＂路感＂。通过对EPS的结构和原理分析,指出EPS是未来汽车技术发展的必然趋势。     

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.     

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 the Lane Keeping Control System Using State-Varying Surface for Vulnerable Road Users

This paper proposes a new Lane Keeping Assist (LKA) system based on the integrated control strategy with AFS and BTV. To be specific, the steering controller calculates the gear ratio of AFS to align with the target lane whereas the braking controller determines differential brake pressure using Sliding Mode Control (SMC) theory according to the state-varying sliding surface with Fuzzy model. In recent years, auto industries have produced the lane keeping applications to prevent lane departure caused by drivers’ distractions or drowsiness. To also prevent wrist injury in drivers while steering, current LKA systems limit the output values of steering-wheel assist torque. This limiting mechanism, however, can cause a problem that cannot follow a road curvature when an older driver overexerts an inappropriate control effort. A new LKA system of the AFS and BTV integrated controller has since been drafted to solve this problem, and validated its performance in regards to the test conditions given with various driver models.     

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.     

Drowsy behavior detection based on driving information

Drowsy behavior is more likely to occur in sleep-deprived drivers. Individuals’ drowsy behavior detection technology should be developed to prevent drowsiness related crashes. Driving information such as acceleration, steering angle and velocity, and physiological signals of drivers such as electroencephalogram (EEG), and eye tracking are adopted in present drowsy behavior detection technologies. However, it is difficult to measure physiological signal, and eye tracking requires complex experiment equipment. As a result, driving information is adopted for drowsy driving detection. In order to achieve this purpose, driving experiment is performed for obtaining driving information through driving simulator. Moreover, this paper investigates effects of using different input parameter combinations, which is consisted of lateral acceleration, longitudinal acceleration, and steering angles with different time window sizes (i.e. 4 s, 10 s, 20 s, 30 s, 60 s), on drowsy driving detection using random forest algorithm. 20 s-size datasets using parameter combination of accelerations in lateral and longitudinal directions, compared to the other combination cases of driving information such as steering angles combined with lateral and longitudinal acceleration, steering angles only, longitudinal acceleration only, and lateral acceleration only, is considered the most effective information for drivers’ drowsy behavior detection. Moreover, comparing to ANN algorithm, RF algorithm performs better on processing complex input data for drowsy behavior detection. The results, which reveal high accuracy 84.8 % on drowsy driving behavior detection, can be applied on condition of operating real vehicles.