Human-Centered Design of an Acc-With-Braking and Forward-Crash-Warning System

This paper addresses the development of driver assistance systems whose functional purposes are to provide both adaptive cruise control (ACC) and forward collision warning (FCW). The purpose of the paper is to combine concepts from human factors psychology, vehicle-dynamics, and control theory, thereby contributing to the body of knowledge and understanding concerning human-centered approaches for designing and evaluating driver assistance systems. Conceptual and experimental results pertaining to driving manually and with the assistance of ACC and FCW are presented. The following human-centered aspects of driver-assistance systems are analyzed and presented: the looming effect; including rule-based and skill-based behavior in the design of ACC systems; using desired dynamics in controlling the driving process; braking rules that trade headway range for deceleration level; and collision-warning rules based on two different stress indicators. Field-test data are examined to justify and verify the parametric values selected for use in human-centered ACC systems. Measured data from on-road driving are used to evaluate the performance of proposed FCW systems in braking situations. The paper concludes with observations concerning the difficulty of developing a clear understanding of when and why drivers brake.     

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.     

Unified decision-making and control for highway collision avoidance using active front steer and individual wheel torque control

ABSTRACT

Collision avoidance is a crucial function for all ground vehicles, and using integrated chassis systems to support the driver presents a growing opportunity in active safety. With actuators such as in-wheel electric motors, active front steer and individual wheel brake control, there is an opportunity to develop integrated chassis systems that fully support the driver in safety critical situations. Here we consider the scenario of an impending frontal collision with a stationary or slower moving vehicle in the same driving lane. Traditionally, researchers have approached the required collision avoidance manoeuver as a hierarchical scheme, which separates the decision-making, path planning and path tracking. In this context, a key decision is whether to perform straight-line braking, or steer to change lanes, or indeed perform combined braking and steering. This paper approaches the collision avoidance directly from the perspective of constrained dynamic optimisation, using a single optimisation procedure to cover these aspects within a single online optimisation scheme of model predictive control (MPC). While the new approach is demonstrated in the context of a fully autonomous safety system, it is expected that the same approach can incorporate driver inputs as additional constraints, yielding a flexible and coherent driver assistance system.     

电动汽车复合制动控制研究现状综述

电动汽车复合制动由电机再生制动与机械摩擦制动两部分构成,其控制性能直接影响车辆的能量利用效率、制动安全性以及舒适性。围绕静态制动转矩分配控制、动态复合制动协调控制、制动换挡控制、智能辅助驾驶中的复合制动控制4个方面的研究现状与关键技术展开综述,并对复合制动控制未来研究方向进行了展望。对文献的梳理分析表明：制动转矩分配决定着复合制动系统能量回收能力与车辆制动稳定性,基于规则的分配策略面对复杂多变工况自适应性欠佳,而基于优化的分配策略各方面性能表现良好,但需要兼顾控制实时性与优化效果;利用电机响应迅速与控制精确的优势完成复合制动协调控制,能够提升制动模式切换过渡工况与紧急制动工况的控制性能,改善驾驶舒适性;制动过程中实施合理换挡可以进一步提升能量回收效率,同时通过补偿控制解决换挡过程中动力中断和转矩冲击等问题,保证换挡平顺性;随着电动汽车智能化和网联化发展,复合制动控制与驾驶人辅助系统相结合有助于在保证系统功能的同时实现能量回收效益最大化。     

考虑驾驶人风格的跟车预警规则研究

为研究驾驶人的跟车特性及探究可适用于不同风格驾驶人的跟车预警规则,为自动驾驶车辆开发可满足不同用户驾驶需求和驾乘体验的主动安全预警系统,选取50名被试驾驶人开展实车试验,采集驾驶人跟车行为表征参数并基于雷达数据确定跟车事件提取规则。选取平均跟车时距和平均制动时距为二维向量,使用基于K-means聚类结果的高斯混合模型将驾驶人聚类为3种风格类型(冒进型、平稳型、保守型)。通过分析3组驾驶人的跟车及制动数据,将不同类型驾驶人的制动时距分位数作为跟车预警阈值,结合实际预警数据及不同制动时距分位数对应的预警正确率,对现有跟车预警规则进行调整,以适应不同类型驾驶人的驾驶需求。研究结果表明:3组驾驶人的平均跟车时距和平均制动时距差异显著,冒进型驾驶人倾向于选择较小的跟车时距和制动时距,保守型驾驶人的跟车时距和制动时距则普遍较大;3组驾驶人的实际跟车预警次数为215次,驾驶人采取制动操作而系统未予以预警的次数为329次,系统整体预警正确率为21.9%,漏警率为87.5%,通过分析信息熵等判定当前预警规则并不合理;将每类驾驶人制动时距的10%分位数作为阈值时的预警效果较好,调整后的跟车预警规则能在一定程度上适应不同的驾驶人类型。     

驾驶辅助技术在事故预防中的应用效果

越来越多的车型都搭载了驾驶辅助系统,部分驾驶人对这类系统的实际效果持怀疑态度。本文通过调研国内外相关研究,对几类主流驾驶辅助系统的事故预防效果进行综述。与相同条件下未安装驾驶辅助系统的车辆对比,研究人员发现多数驾驶辅助系统在事故预防方面发挥了明显的作用,降低了相关的事故率、保险索赔率,其中前向和后向的自动紧急制动系统效果最为显著。此外,驾驶人对驾驶辅助系统的接受度以及响应度均会影响事故预防效果。     

Analysis of Driver's Decelerating Strategy in a Car-following Situation

This report describes a decelerating driver-model expressed by driving mode transition in car-following situations. The assumptions for constructing the model are that decelerating strategy of a driver is classified into several simple driving modes and that a driver changs his driving modes based on his perceptible characteristics and experiential rules. Deceleration action is divided into three states; following, standing and braking, which are applied to the model. The model has two paths for driver's decelerating action, one of which is selected by the driver based on the perceptible characteristics and experiential rules. The suitability of the model has been experimentally verified.     

Evaluation of preview G-Vectoring control to decelerate a vehicle prior to entry into a curve

This paper describes the development of the braking assistance system based on a “G-Vectoring” concept. The present work focuses in particular on “Preview G-Vectoring Control” (PGVC), which is based on the “G-Vectoring Control” (GVC) scheme. In GVC, the longitudinal-acceleration control algorithm is based on the actual lateral jerk. PGVC decelerates a vehicle before it enters a curve, and is based on a new longitudinal-acceleration control algorithm which uses predicted and actual lateral jerk. Using the predicted lateral jerk makes it possible to decelerate the vehicle prior to curve entry. This deceleration can emulate a driver’s deceleration as the vehicle approaches a curve entry. PGVC is based on such deceleration algorithms and enables automatic deceleration similar to the action of a driver. It is thus possible to significantly improve the driver’s feeling when this system is activated. Driving tests with this new control system on snowy-winding course confirmed that the automatic brake control quality improved considerably compared to manual driver control considering both lap time and ride quality. These results indicate that PGVC can be a useful braking assistance system not only to improve the driver’s handling performance but also to reduce the brake-task during driving on winding roads.     

A driver-adaptive stability control strategy for sport utility vehicles

Conventional vehicle stability control (VSC) systems are designed for average drivers. For a driver with a good driving skill, the VSC systems may be redundant; for a driver with a poor driving skill, the VSC intervention may be inadequate. To increase safety of sport utility vehicles (SUVs), this paper proposes a novel driver-adaptive VSC (DAVSC) strategy based on scaling the target yaw rate commanded by the driver. The DAVSC system is adaptive to drivers’ driving skills. More control effort would be exerted for drivers with poor driving skills, and vice versa. A sliding mode control (SMC)-based differential braking (DB) controller is designed using a three degrees of freedom (DOF) yaw-plane model. An eight DOF nonlinear yaw-roll model is used to simulate the SUV dynamics. Two driver models, namely longitudinal and lateral, are used to ‘drive’ the virtual SUV. By integrating the virtual SUV, the DB controller, and the driver models, the performance of the DAVSC system is investigated. The simulations demonstrate the effectiveness of the DAVSC strategy.     

Development of a path planning system using mean shift algorithm for driver assistance

The longitudinal and lateral vehicle control techniques have been widely used in several active driver assistance systems. The adaptive cruise control, lane keeping assistant control, vehicle platooning and stop-and-go control are typical examples of the most important applications. In this study, a novel path planning method is proposed considering the driving environment such as road shape, ego vehicle and surrounding vehicles’ movement. The relative distance and velocity between the ego vehicle and surrounding vehicles are identified with respect to the predicted lane shape in front of the ego vehicle. Based on the identified information, the road shape and surrounding vehicles are mapped into the intensity image and the desired vector for the ego vehicle’s movement is determined by the maximum intensity density tracing method. The desired vehicle path is followed by the acceleration/deceleration control and the steering assist control, respectively. In order to evaluate the performance of the proposed system, simulations are conducted and compared with ACC systems.     

电子楔式制动器

未来的驾驶辅助系统将不仅仅对交通状况进行监控．它们也将积极协助处于紧急事件之中的司机，一种快速．智能的制动系统将成为制造新一代驾驶辅助系统的基础之一．     

Advanced emergency braking under split friction conditions and the influence of a destabilising steering wheel torque

The steering system in most heavy trucks is such that it causes a destabilising steering wheel torque when braking on split friction, that is, different friction levels on the two sides of the vehicle. Moreover, advanced emergency braking systems are now mandatory in most heavy trucks, making vehicle-induced split friction braking possible. This imposes higher demands on understanding how the destabilising steering wheel torque affects the driver, which is the focus here. Firstly, an experiment has been carried out involving 24 subjects all driving a truck where automatic split friction braking was emulated. Secondly, an existing driver–vehicle model has been adapted and implemented to improve understanding of the observed outcome. A common conclusion drawn, after analysing results, is that the destabilising steering wheel torque only has a small effect on the motion of the vehicle. The underlying reason is a relatively slow ramp up of the disturbance in comparison to the observed cognitive delay amongst subjects; also the magnitude is low and initially suppressed by passive driver properties.     

Driver propensity characterization for different forward collision warning times

The forward collision warning system, which warns danger to the driver after sensing possibility of crash in advance, has been actively studied recently. Such systems developed until now give a warning, regardless of driver’s driving propensity. However, it’s not reasonable to give a warning to every driver at the same time because drivers are different in driving propensity. In this study, to give a warning to each driver differently, three metrics classifying driver’s driving propensity were developed by using the driving data on a testing ground. These three metrics are the predicted time headway, required deceleration divided by the deceleration of the leading vehicle, and the resultant acceleration divided by the deceleration of the leading vehicle. Driving propensity was divided into 3 groups by using these metrics for braking and steering cases. In addition, these metrics were verified by making sure that braking propensity could be classified on public roads as well.     

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.     

基于双模式执行器的商用车自适应巡航控制系统

为实现商用车自适应巡航控制(ACC)系统的功能,开发了双模式制动执行装置和电子油门控制装置,即基于高速开关阀的商用车气压电控辅助制动系统和双模式油门控制系统,可以实现驾驶员和ACC系统的协同切换控制。在此基础上,以某商用车为对象,设计了ACC系统,结合比例-积分控制器和Smith预估补偿器设计了ACC的下位控制算法。结果表明:该ACC系统速度稳态跟踪误差小于1 m.s-1,距离稳态跟踪误差小于1.5 m;同时油门执行器和制动执行器具有安装方便、与原车电子油门及气压制动系统兼容性好的优点。     

考虑肌电信号的驾驶人弯道操纵行为分析

驾驶人是"人-车-路"闭环系统中的核心。近年来，研发人性化、个性化的汽车驾驶辅助系统逐渐成为行业热点。为了更加透彻地理解弯道驾驶行为特性，为弯道驾驶辅助系统提供功效评估与优化，提出了一种考虑肌电信号的驾驶人弯道行驶过程操纵行为分析方法。招募12名驾驶人在试验场标准路面上进行实车试验，其中包含6名专业试车师与6名普通驾驶人，要求驾驶人分别以30，40，50 km·h^-1的不同初速度驶入U形弯道并自由驾驶。试验过程中记录驾驶人颈部肌电信号数据和车辆运动状态数据，分析转弯行驶车辆侧向运动对不同驾驶能力的驾驶人生理体验的影响，同时进一步探讨不同类型驾驶人在不同入弯速度条件下颈部肌电信号与侧向加速度的关联差异特性。试验结果表明：相同工况下，专业驾驶人和普通驾驶人颈部肌电特征值存在显著差异，专业驾驶人颈部肌电信号特征与车辆侧向加速度呈现一定的线性关系；随着驾驶任务难度的增加，驾驶能力好的驾驶人能够较好地适应任务的变化，在进行纵侧向耦合操纵时能够较好地协调身体生理反应与车辆侧向运动保持较好的关联特性。研究成果为进一步探索并完善驾驶体验评价方法提供了新的研究思路，同时，可为汽车辅助驾驶系统功能设计与智能汽车行驶性能的用户体验测评提供技术支撑。     

Comparison of driving characteristics between drivers in Korea and in the united states of America based on driver-vehicle interaction field database

Research and development involving intelligent vehicles of today is geared to safe, driver-friendly and sensitive vehicles that provide a driver with a pleasant and convenient driving environment while preventing him or her from possible risks of accident. In developing convenient and safe vehicles, research on drivers’ driving patterns, reactions and state characteristics depending on road conditions in actual field is essential in order to devise more driver-friendly intelligent vehicles. This paper describes how a driver-vehicle interaction (DVI) field database is built in order to obtain a driver’s input in normal road driving condition on highways, country roads, and city roads, and his or her state information, as well as data on the vehicle and traffic conditions. And the newly built database is compared with the RDCW FOT database established by UMTRI of the US for analysis to suggest that the driving tendencies of drivers in Korea and the road driving conditions are not the same as those in the US, reconfirming the need to establish a DVI field database, which will be used for the development of intelligent vehicles suitable for the Korean environment. The DVI data collected from actual driving in field are anticipated to be widely utilized as basic data for research on various intelligent driving safety systems, advanced driver assistance systems (ADAS) and human-vehicle interface (HVI) that are suitable for the driving environment in Korea.     

In-depth understanding of lane changing interactions for in-vehicle driving assistance systems

Lane-changing events are often related with safety concern and traffic operational efficiency due to complex interactions with neighboring vehicles. In particular, lane changes in stop-and-go traffic conditions are of keen interest because these events lead to higher risk of crash occurrence caused by more frequent and abrupt vehicle acceleration and deceleration. From these perspectives, in-depth understanding of lane changes would be of keen interest in developing in-vehicle driving assistance systems. The purpose of this study is to analyze vehicle interactions using vehicle trajectories and to identify factors affecting lane changes with stop-and-go traffic conditions. This study used vehicle trajectory data obtained from a segment of the US-101 freeway in Southern California, as a part of the Next Generation Simulation (NGSIM) project. Vehicle trajectories were divided into two groups; with stop-and-go and without stop-and-go traffic conditions. Binary logistic regression (BLR), a well-known technique for dealing with the binary choice condition, was adopted to establish lane-changing decision models. Regarding lane changes without stop-and-go traffic conditions, it was identified based on the odd ratio investigation that he subject vehicle driver is more likely to pay attention to the movement of vehicles ahead, regardless of vehicle positions such as current and target lanes. On the other hand, the subject vehicle driver in stop-and-go traffic conditions is more likely to be affected by vehicles traveling on the target lane when deciding lane changes. The two BLR models are adequate for lane-changing decisions in normal and stop-and-go traffic conditions with about 80 % accuracy. A possible reason for this finding is that the subject vehicle driver has a tendency to pay greater attention to avoiding sideswipe or rear-end collision with vehicles on the target lane. These findings are expected to be used for better understanding of driver’s lane changing behavior associated with congested stop-and-go traffic conditions, and give valuable insights in developing algorithms to process sensor data in designing safer lateral maneuvering assistance systems, which include, for example, blind spot detection systems (BSDS) and lane keeping assistance systems (LKAS).     

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.