Detecting Driver Normal and Emergency Lane-Changing Intentions With Queuing Network-Based Driver Models

Driver intention detection is an important component in human-centric driver assistance systems. This article proposes a novel method for detecting driver normal and emergency left- or right-lane-changing intentions by using driver models based on the queuing network cognitive architecture. Driver lane-changing and lane-keeping models are developed and used to simulate driver behavior data associated with 5 kinds of intentions (i.e., normal and emergency left- or right-lane-changing and lane-keeping intentions). The differences between 5 sets of simulated behavior data and the collected actual behavior data are computed, and the intention associated with the smallest difference is determined as the detection outcome. The experimental results from 14 drivers in a driving simulator show that the method can detect normal and emergency lane-changing intentions within 0.325 s and 0.268 s of the steering maneuver onset, respectively, with high accuracy (98.27% for normal lane changes and 90.98% for emergency lane changes) and low false alarm rate (0.294%).     

Performance evaluation of decision making strategies for an embedded lane departure warning system

An on‐board lane departure warning system embedded in a vehicle is composed of a localization module and a decision making module. The decision making module detects unintended lane departure so as to warn the driver of the danger. The performance of a decision making module is crucial to the performance of the total embedded system. This article proposes two heuristic decision making strategies: a lateral offset (LO) based strategy and a time‐to‐lane crossing (TLC) based strategy. The performance criteria of decision making strategies are proposed as: (1) false alarm rate, and (2) alarm triggering time (ATT). Numerical parameters of both strategies are optimized through numerical simulation, taking the performance criteria into consideration. The proposed strategies are incorporated into the prototype system and evaluated in real expressway experiments. The comparative study of both methods with experimental results shows the applicability of the on‐board lane departure warning system. © 2002 Wiley Periodicals, Inc.     

Effects of single versus multiple warnings on driver performance

OBJECTIVE: To explore how a single master alarm system affects drivers' responses when compared with multiple, distinct warnings. BACKGROUND: Advanced driver warning systems are intended to improve safety, yet inappropriate integration may increase the complexity of driving, especially in high workload situations. This study investigated the effects of auditory alarm scheme, reliability, and collision event type on driver performance. METHOD: Using a 2 x 2 x 4 mixed factorial design, we investigated the impact of two alarm schemes (master vs. individual) and two levels of alarm reliability (high and low) on distracted drivers' performance across four collision event types (frontal collision warnings, left and right lane departure warnings, and warnings for a fast-approaching following vehicle). RESULTS: Participants' reaction times and accuracy rates were significantly affected by the type of collision event and alarm reliability. The use of individual alarms, rather than a single master alarm, did not significantly affect driving performance in terms of reaction time or response accuracy. CONCLUSION: Even though a master alarm is a relatively uninformative warning, it produced statistically no different reaction times or accuracy results when compared with information-rich auditory icons, some of which were spatially located. In addition, unreliable alarms negatively impacted driver performance, regardless of event type or alarm scheme. APPLICATION: These results have important implications for the development and implementation of multiple driver warning systems.     

Announcing Automated Lane Changes: Active Vehicle Roll Motions as Feedback for the Driver

ABSTRACT

As supervisor of the automation system during partially automated driving, it is essential for the driver to have a good awareness of the automation to fulfill this new task sufficiently. Therefore, feedback about intentions of the automation, for example lane changes, is crucial. So far, this feedback is mainly presented visually to the driver. Conversely, in this article, feedback for announcing lane changes is realized via active vehicle roll motions. Several designs are implemented in an automated test vehicle and are evaluated in four different driving scenarios on a test track. Totally, 39 participants rated the vehicle roll motions, for example, regarding the items roll direction, intensity, usefulness, and the predictability of the driving behavior. The results show that active roll motions as feedback for announcing automated lane changes should be clearly perceptible and are considered useful, not misleading, and support the drivers regarding their mode/system awareness.     

Shared control for lane departure prevention based on the safe envelope of steering wheel angle

The ability to prevent lane departure has become an important feature for commercialized vehicles. This paper proposes a shared steering assistance strategy based on a safe envelope of steering wheel angle (SWA). This solves the human-machine conflict issue in lane departure prevention (LDP) system which uses steering control to help the driver keep the vehicle within the correct lane. The system combines a driver steering control model, current vehicle states and vehicle-road deviation. The desired SWAs are calculated when the driver intends to drive along the left or right side of the lane, and then the two angles are used to generate the safe envelope. Next, a driver intention estimator is designed to predict driver’s intended SWA and the assistance control is activated by judging whether the driver intended SWA is go beyond the safe envelope. Finally, a H∞ controller and a disturbance observer are developed to determine the assistance torque. In this way, the SWA is limited to safe values to mitigate lane departure and the controller intervention is minimized. The effectiveness of the proposed method is evaluated via numerical simulation with different driving scenarios and human-in-the-loop experiment on a driving simulator. The obtained results show that this method not only can avoid lane departures effectively, but also ensures a good human-machine cooperative performance.     

基于机器视觉的轻量级驾驶辅助系统

为了解决计算机视觉应用中数据量大、算法复杂的问题,根据道路结构特征和车辆行为特征,采用单个摄像头作为传感器,实现了一种轻量级的安全辅助驾驶系统。首先采用改进的边缘提取算法和车道线检测算法对摄像机内外参数进行离线标定;接着根据标定结果在二维平面图像上采用标识出实际空间距离的多窗口划分方法,并按不同的车间距将不同窗口划分为不同安全系数的区域,以赋予道路视觉检测的几何先验知识;当区域中出现障碍物时发出相应警示信息进行安全驾驶辅助,能为智能辅助驾驶提供轻量级的视觉检测平台。以便携式计算机和固定在车内的摄像头作为实验装置,在城市道路上进行车载实验。系统在车载实验中能够快速地提取车辆两侧的车道线,并利用离线标定的结果快速生成不同安全系数的警示区域,其中车辆在车道内正常行驶时的误检率和漏检率很小,可以忽略不计。与传统的驾驶辅助系统相比,本系统计算量大大降低,检测流程得到简化,可实现轻量级的车道和车辆检测,为系统在嵌入式系统上的实现奠定基础。     

基于图像处理的车辆防偏防追尾预警系统研究

针对交通事故的不断上升,设计一种基于图像处理的车辆防偏防追尾预警系统。首先利用改进的Hough变换检测出前方车道线并进行预警决策;然后在此车道区域内根据车底部阴影的梯度特征确定前方可能存在的车辆区域,通过卡尔曼滤波器跟踪检测到的目标,并利用归一化转动惯量做车辆验证;最后根据世界坐标系和图像坐标系之间的几何映射关系测定与前车的距离,进而与计算得出的安全距离对比从而实现报警功能。实验结果表明,该系统能够有效的识别出车道线和车辆,并能很好的判断车道线偏离情况和测量车间距,从而实现预警输出。     

Lane-keeping assistance control algorithm using differential braking to prevent unintended lane departures

This paper describes a hierarchical lane keeping assistance control algorithm for a vehicle. The proposed control strategy consists of a supervisor, an upper-level controller and a lower-level controller. The supervisor determines whether lane departure is intended or not, and whether the proposed algorithm is activated or not. To detect driver′s lane change intention, the steering behavior index has been developed incorporating vehicle speed and road curvature. To validate the detection performance on the lane change intention, full-scale simulator tests on a virtual test track (VTT) are conducted under various driving situations. The upper-level controller is designed to compute the desired yaw rate for the lane departure prevention, and for the guidance with ride comfort. The lower-level controller is designed to compute the desired yaw moment in order to track the desired yaw rate, and to distribute it into each tire′s braking force in order to track the desired yaw moment. The control allocation method is adopted to distribute braking forces under the actuator’s control input limitation. The proposed lane keeping assistance control algorithm is evaluated with human driver model-in-the-loop simulation and experiments on a real vehicle.     

Turn-Intent Analysis Using Body Pose for Intelligent Driver Assistance

Human-centric, pervasive computing environments, with integrated sensing, processing, networking, and displays, provide an appropriate framework for developing effective driver-assistance systems. Also essential when developing such systems are systematic efforts to understand and characterize driver behavior. In an attempt to make such a predictive turn-assistance safety system a reality, we equipped an experimental vehicle with cameras and sensors to capture the vehicle dynamics, view of the road ahead, and driver's body pose. We investigated how and to what extent we could use body-pose information to detect and predict driver activities. We analyzed the detection performance of a two-class pattern classifier using receiver-operator-characteristic curves, which describe the classifier's ability to suppress missed detections and false alarms. The curves provide a ratio indicating the system's attainable proactivity (ability to foresee a user's needs) versus its transparency (ability to avoid user annoyance). Our goal is to eventually develop vision-based body-pose-recovery and behavior-recognition algorithms for driver-assistance systems     

Shared Control of Highly Automated Vehicles Using Steer-By-Wire Systems

A shared control of highly automated Steer-by-Wire system is proposed for cooperative driving between the driver and vehicle in the face of driver's abnormal driving. A fault detection scheme is designed to detect the abnormal driving behaviour and transfer the control of the car to the automatic system designed based on a fault tolerant model predictive control (MPC) controller driving the vehicle along an optimal safe path. The proposed concept and control algorithm are tested in a number of scenarios representing intersection, lane change and different types of driver's abnormal behaviour. The simulation results show the feasibility and effectiveness of the proposed method.      

Video-Based Driver Assistance--From Basic Functions to Applications

Image sequences recorded with cameras mounted in a moving vehicle provide information about the vehicle's environment which has to be analysed in order to really support the driver in actual traffic situations. One type of information is the lane structure surrounding a vehicle. Therefore, we systematically developed and investigated driver assistance functions which make explicit use of the lane structure represented by lane borders and lane markings. With increasing computing power of standard PCs it was possible to realize more complex driver assistance with general purpose hardware. Investigations with a video-based lane departure warning system and a lane change assistant for highways will be discussed in detail.We integrated our lane keeping assistant in some experimental cars and performed systematic experiments in real traffic situations which enables the experience of video-based driver assistance on a high level--the action of a system. This allows us to assess whether a driver assistance system really understands the actual traffic situation which is the basis for reliable systems accepted by the user.     

基于视觉的车道跑偏检测方法研究及仿真

为了保证辅助驾驶技术行车的安全,在分析了基于视觉的车道跑偏检测方法的具体步骤的基础上,首先提出了利用由计算机视觉获得的车道标志线来进一步获得车-路关系的方法,并推导了几种车道跑偏判据TLC(time to lane crossing)的计算公式;然后利用“预瞄最优曲率模型”来仿真人-车-路的关系,并验证了当人的状态发生变化时,TLC判据可以有效地提供报警的效果;最后在红旗自主驾驶样车的视觉导航系统中进行了实验,实验结果表明,上述分析和仿真是可行的。     

Collision-avoidance systems PRORETA: Situation analysis and intervention control

After a discussion of passive and active safety systems for automobiles and accident statistics it follows that a further progress in the reduction of accidents can be especially expected by next-generation driver-assistance systems with a sequence of warnings and active interventions. PRORETA is an Industry–University research project with the goal to develop steps towards accident-free driving. The first project considers two vehicles moving in the same direction. The other vehicle is detected by a fusion of LIDAR and camera data providing the system with relative speeds and distance. If the driver does not react to an obstacle on the own lane, the system automatically triggers an emergency braking and/or swerving to avoid a collision. This includes e.g. a fast and precise evasive trajectory control by automatic steering.The second project is dedicated to vehicles moving in opposite directions performing an overtaking maneuver on rural roads. The own vehicle detects the velocities and distances to the preceding and oncoming vehicle by RADAR and lane markings, etc. with a camera. The measured data of the two sensors undergo a sensor fusion with Kalman filters. The overtaking maneuver is predicted by using the measured data of all three vehicles. If an accident-free overtaking is in danger, warnings are given to the driver and if the driver does not react in time a full braking of the own vehicle is triggered such that the driver can turn back behind the overtaken vehicle.The contribution gives a survey on the developed strategies and some basic calculated features and control systems. Measured data are shown and give an impression of the driving experiments on the runway of an air field.     

A digital-driving system for smart vehicles

In the wake of the computer and information technology revolutions, vehicles are undergoing dramatic changes in their capabilities and how they interact with drivers. Although some vehicles can decide to either generate warnings for the human driver or control the vehicle autonomously, they must usually make these decisions in real time with only incomplete information. So, human drivers must still maintain control over the vehicle. I sketch a digital driving behavior model. By simulating and analyzing driver behavior during different maneuvers such as lane changing, lane following, and traffic avoidance, researchers participating in the Beijing Institute of Technology's digital-driving project will be able to examine the possible correlations or causal relations between the smart vehicle, IVISs, the intelligent road-traffic-information network, and the driver. We aim to successfully demonstrate that a digital-driving system can provide a direction for developing human-centered smart vehicles.     

Driver fatigue detection using a genetic algorithm

Nowadays, many traffic accidents occur due to driver fatigue. Driver fatigue detection based on computer vision is one of the most hopeful applications of image recognition technology. There are several factors that reflect driver's fatigue. Many efforts have been made to develop fatigue monitoring, but most of them focus on only a single behavior, a feature of the eyes, or a head motion, or mouth motion, etc. When fatigue monitoring is implemented on a real model, it is difficult to predict the driver fatigue accurately or reliably based only on a single driver behavior. Additionally, the changes in a driver's performance are more complicated and not reliable. In this article, we represent a model that simulates a space in a real car. A web camera as a vision sensor is located to acquire video-images of the driver. Three typical characteristics of driver fatigue are involved, pupil shape, eye blinking frequency, and yawn frequency. As the influences of these characteristics on driver fatigue are quite different from each other, we propose a genetic algorithm (GA)-based neural network (NN) system to fuse these three parameters. We use the GA to determine the structure of the neural network system. Finally, simulation results show that the proposed fatigue monitoring system detects driver fatigue probability more exactly and robustly. This work was presented in part at the 11th International Symposium on Artificial Life and Robotics, Oita, Japan, January 23–25, 2006     

基于DSP的车道偏离检测与车辆前向车距检测

基于智能交通的快速发展,研究了在高速路段下基于机器视觉的车道偏离检测与车辆前向安全车距检测技术.首先固定车载相机,通过相机标定获取相机的内参数和外参数,进而设计车距检测模型.该模型不但能够检测出前方车辆与无人车的距离,还能计算出前方车辆相对于摄像机光轴的偏转角度.接着在CCP偏离检测算法的基础上,设定安全和报警区来建立车道偏离模型,并对当前车辆的偏离结果作出正常行驶的评判.最后借助TI的DVSDK组件包将算法移植到嵌入式平台DSP-DM3730上测试.实验表明,本文设计的车距检测模型和车道偏离模型在解决无人车的前向防撞检测和车道偏离检测等问题上具有较好的参考价值.     

速度与挡位匹配提醒系统的研究与设计

近年来,交通运输已成为我们生活中比较重要的一部分,私家车及驾驶员的数量均越来越多。本文针对这种情况,为驾驶员,尤其是初学者提供一套手动挡汽车速度与挡位匹配的控制系统。此系统以单片机为控制核心,结合监测、控制、显示、语音播报ISD4004、报警等模块实现行车过程的实时监测、控制及提醒。本系统为初学者提供辅助教学,为驾驶员提供经济的行车方式,同时提高我国的汽车行业的竞争力。     

Automation-driver cooperative driving in presence of undetected obstacles

The work presented in this paper describes and discusses the principles of a haptic shared control between a human driver and an Electronic copilot (E-copilot) for a vehicle. The aim of the sharing control is to allow the driver to momentarily take control over the E-copilot without deactivating it nor being constrained, in order to deal with a specific situation such as avoiding an obstacle that has not been detected by the E-copilot. As the E-copilot acts simultaneously on the steering system with the driver, both have to be aware of one another's actions, which means bi-directional communication is essential. In this work, to achieve this goal, we consider the haptical interactions through the steering wheel. The torque applied by the driver on the steering system is used by the E-copilot to take into account the driver's actions while the E-copilot assistance torque is felt by the driver and used by him to understand the system's behavior. This low communication level strongly improves the cooperation between the driver and the E-copilot.The system takes into account the drivers actions thanks to a driver lane keeping model that is added to the road vehicle one in the controller synthesis step. This allows to introduce driver's interaction control variables in such a way that the E-copilot can consider conflicting objectives between the driver and the lane keeping task, and thus handle them.In order to highlight the assets of the approach, a comparison of the behaviors of a simple lane keeping E-copilot to that of a cooperative proposed here is given at the end of this paper. This comparison is achieved through computer simulations and experimental tests with a human driver carried out in the SHERPA-LAMIH interactive dynamic driving simulator. The results of these tests confirm the improvement of the level of cooperation between the human driver and the E-copilot and show that the cooperative E-copilot gives more authority to the human driver especially in hazardous situations.     

Driver steering assistance for lane departure avoidance

In this paper, a steering assistance system is designed and experimentally tested on a prototype passenger vehicle. Its main goal is to avoid lane departures when the driver has a lapse of attention. Based on a concept linking Lyapunov theory with linear matrix inequalities (LMI) optimization, the following important features are ensured during the assistance intervention: the vehicle remains within the lane borders while converging towards the centerline, and the torque control input and the vehicle dynamics are limited to safe values to ensure the passengers’ comfort. Because the steering assistance takes action only if necessary, two activation strategies have been proposed. Both activation strategies were tested on the prototype vehicle and were assessed as appropriate. However, the second strategy showed better reactivity in case of rapid drifting out of the lane.