Back to the future: brake reaction times for manual and automated vehicles

Rear-end collisions are often quoted as being a major cause of road traffic accidents. In response to this, a great deal of ergonomics research effort has been directed towards the analysis of brake reaction times. However, the engineering solution has been to develop advanced systems for longitudinal control, which, it is argued, will mitigate the problem of rear-end collisions. So far, though, there have been few empirical studies to determine how brake reaction times will be affected by such vehicle automation. This paper presents a literature review summarizing the current state of knowledge about driver responses in non-automated vehicles. The review covers driver factors, vehicle factors and situational factors. Following the review, some empirical data are presented from a driving simulator experiment assessing brake reaction times of skilled and unskilled drivers under two different levels of automation. When compared to previous data gathered during manual driving, there seems to be a striking increase in reaction times for these automated conditions. Implications for the design and safety of automated vehicle systems are discussed.     

Human–Vehicle Cooperation in Automated Driving: A Multidisciplinary Review and Appraisal

ABSTRACT

To draw a comprehensive and cohesive understanding of human–vehicle cooperation in automated driving, a review is made on key studies in human–robot interaction and human factors. Throughout this article, insight is provided into how human drivers and vehicle systems interplay and influence each other. The limitations of technology-centered taxonomies of automation are discussed and the benefits of accounting for human agents are examined. The contributions of machine learning to automated driving and how critical models in human-system cooperation can inform the design of a more symbiotic relationship between driver and vehicle are investigated. Challenges in the human element to enable the safe introduction of road automation are also discussed. Particularly, the unintended consequences of vehicle automation on driver’s workload, situation awareness and trust are examined, and the social interactions between driver, vehicle, and other road users are investigated. This review will help professionals shape future directions for safer and more efficient and effective human–vehicle cooperation.     

Driver–vehicle cooperation: a hierarchical cooperative control architecture for automated driving systems

The concept of automated driving changes the way humans interact with their cars. However, how humans should interact with automated driving systems remains an open question. Cooperation between a driver and an automated driving system—they exert control jointly to facilitate a common driving task for each other—is expected to be a promising interaction paradigm that can address human factors issues caused by driving automation. Nevertheless, the complex nature of automated driving functions makes it very challenging to apply the state-of-the-art frameworks of driver–vehicle cooperation to automated driving systems. To meet this challenge, we propose a hierarchical cooperative control architecture which is derived from the existing architectures of automated driving systems. Throughout this architecture, we discuss how to adapt system functions to realize different forms of cooperation in the framework of driver–vehicle cooperation. We also provide a case study to illustrate the use of this architecture in the design of a cooperative control system for automated driving. By examining the concepts behind this architecture, we highlight that the correspondence between several concepts of planning and control originated from the fields of robotics and automation and the ergonomic frameworks of human cognition and control offers a new opportunity for designing driver–vehicle cooperation.

     

基于MEMS和GPS的驾驶行为和车辆状态监测系统设计

为了适应智能车辆辅助驾驶系统对驾驶和车辆状态监测的要求,利用MEMS惯性传感器自主设计了微惯性测量单元,并结合GPS设计了一种驾驶行为和车辆状态监测系统,实现对驾驶员操纵动作的感知、汽车6自由度运动状态参数和汽车运行车速的实时监测。介绍了MEMS传感器的选型,设计,安装和布置。实车道路实验结果表明:系统对驾驶员踩踏刹车踏板、离合器踏板和变换档位的操纵动作的感知效果较好,侧向加速度和方向盘转角的理论识别曲线与实车实验曲线在趋势上比较吻合。该系统为开发驾驶人员操纵动作自动识别系统提供理论基础和技术支持,也可为提高汽车行驶性和安全性提供重要的理论依据和工程应用指导。     

Making driver modeling attractive

Automobile industry and academic researchers expend considerable effort to develop driver assistance systems. DASs are aware of certain driving situations and support drivers through information, warnings, or even intervention. Many DAS applications are safety oriented, such as lane departure warning systems. Some are comfort oriented, such as automated parking assistants. Moreover, DAS human-machine interfaces must support careful communication with potentially taxed drivers. Driver models support DAS design in several ways. Our work focuses on modeling the tactical level of driving decisions, such as when to brake and whether to accelerate and pass another vehicle. Such decisions are based on local, instantaneously available environmental information about the road and other cars in the same or an adjacent lane.     

Hazard-evaluation-oriented Moving Horizon Parallel Steering Control for Driver-Automation Collaboration During Automated Driving

Prompted by emerging developments in connected and automated vehicles, parallel steering control, one aspect of parallel driving, has become highly important for intelligent vehicles for easing the burden and ensuring the safety of human drivers. This paper presents a parallel steering control framework for an intelligent vehicle using moving horizon optimization. The framework considers lateral stability, collision avoidance and actuator saturation and describes them as constraints, which can blend the operation of a human driver and a parallel steering controller effectively. Moreover, the road hazard and the steering operation error are employed to evaluate the operational hazardous of an intelligent vehicle. Under the hazard evaluation, the intelligent vehicle will be mainly operated by the human driver when the vehicle operates in a safe and stable manner. The automated steering driving objective will play an active role and regulate the steering operations of the intelligent vehicle based on the hazard evaluation. To verify the effectiveness of the proposed hazard-evaluation-oriented moving horizon parallel steering control approach, various validations are conducted, and the results are compared with a parallel steering scheme that does not consider automated driving situations. The results illustrate that the proposed parallel steering controller achieves acceptable performance under both conventional conditions and hazardous conditions.      

The ergonomic value of a bidirectional haptic interface when driving a highly automated vehicle

Advances in technology have fueled the development of driver assistance systems. Even today, these systems can take over parts of the driving task. However, the interface becomes more and more complex with an increasing number of functions. One way to reduce such complexity is to venture the haptic channel. While haptic feedback in lateral direction is comparatively easy to realize via the steering wheel, the longitudinal direction forms a challenge. With conventional control elements, that is, pedals, haptic interaction can only be partially realized (this is due to the division of accelerator and brake pedals). Haptic signals, like forces added to the accelerator pedal, can only transmit information regarding the amount of acceleration, not the desired deceleration. In this context, two-dimensional control elements show great potential regarding future highly automated vehicle driving. Therefore, an experiment conducted at the Institute of Ergonomics of the Technische Universität München investigated the influence of haptic feedback of assistance systems on driving performance when using an active side stick as control element. Additionally, the impact of vehicle vibrations and accelerations were explored. Besides objective performance data, subjective assessment was also reported. The results show that adding assistance significantly improves driving performance. Moreover, subjective ratings indicate a reduction in workload. Accelerations and vibrations, however, had no verifiable effect on the driving performance. This fact was confirmed by the subjects’ subjective assessment. This paper shows that two-dimensional control elements can be a reasonable alternative to steering wheel and pedals when driving a highly automated vehicle.     

Perception,information processing and modeling: Critical stages for autonomous driving applications

Over the last decades, the development of Advanced Driver Assistance Systems (ADAS) has become a critical endeavor to attain different objectives: safety enhancement, mobility improvement, energy optimization and comfort. In order to tackle the first three objectives, a considerable amount of research focusing on autonomous driving have been carried out. Most of these works have been conducted within collaborative research programs involving car manufacturers, OEM and research laboratories around the world. Recent research and development on highly autonomous driving aim to ultimately replace the driver's actions with robotic functions. The first successful steps were dedicated to embedded assistance systems such as speed regulation (ACC), obstacle collision avoidance or mitigation (Automatic Emergency Braking), vehicle stability control (ESC), lane keeping or lane departure avoidance. Partially automated driving will require co-pilot applications (which replace the driver on his all driving tasks) involving a combination of the above methods, algorithms and architectures. Such a system is built with complex, distributed and cooperative architectures requiring strong properties such as reliability and robustness. Such properties must be maintained despite complex and degraded working conditions including adverse weather conditions, fog or dust as perceived by sensors. This paper is an overview on reliability and robustness issues related to sensors processing and perception. Indeed, prior to ensuring a high level of safety in the deployment of autonomous driving applications, it is necessary to guarantee a very high level of quality for the perception mechanisms. Therefore, we will detail these critical perception stages and provide a presentation of usable embedded sensors. Furthermore, in this study of state of the art of recent highly automated systems, some remarks and comments about limits of these systems and potential future research ways will be provided. Moreover, we will also give some advice on how to design a co-pilot application with driver modeling. Finally, we discuss a global architecture for the next generation of co-pilot applications. This architecture is based on the use of recent methods and technologies (AI, Quantify self, IoT …) and takes into account the human factors and driver modeling.     

Optical information for car following: the driving by visual angle (DVA) model

OBJECTIVE: The present study developed and tested a model of car following by human drivers. BACKGROUND: Previous models of car following are based on 3-D parameters such as lead vehicle speed and distance information, which are not directly available to a driver. In the present paper we present the driving by visual angle (DVA) model, which is based on the visual information (visual angle and rate of change of visual angle) available to the driver. METHOD: Two experiments in a driving simulator examined car-following performance in response to speed variations of a lead vehicle defined by a sum of sine wave oscillations and ramp acceleration functions. In addition, the model was applied to six driving events using real world-driving data. RESULTS: The model provided a good fit to car-following performance in the driving simulation studies as well as in real-world driving performance. A comparison with the advanced interactive microscopic simulator for urban and nonurban networks (AIMSUN) model, which is based on 3-D parameters, suggests that the DVA was more predictive of driver behavior in matching lead vehicle speed and distance headway. CONCLUSION: Car-following behavior can be modeled using only visual information to the driver and can produce performance more predictive of driver performance than models based on 3-D (speed or distance) information. APPLICATION: The DVA model has applications to several traffic safety issues, including automated driving systems and traffic flow models.     

A comparison of tactile, visual, and auditory warnings for rear-end collision prevention in simulated driving

OBJECTIVE: This study examined the effectiveness of rear-end collision warnings presented in different sensory modalities as a function of warning timing in a driving simulator. BACKGROUND: The proliferation of in-vehicle information and entertainment systems threatens driver attention and may increase the risk of rear-end collisions. Collision warning systems have been shown to improve inattentive and/or distracted driver response time (RT) in rear-end collision situations. However, most previous rear-end collision warning research has not directly compared auditory, visual, and tactile warnings. METHOD: Sixteen participants in a fixed-base driving simulator experienced four warning conditions: no warning, visual, auditory, and tactile. The warnings activated when the time-to-collision (TTC) reached a critical threshold of 3.0 or 5.0 s. Driver RT was captured from a warning below critical threshold to brake initiation. RESULTS: Drivers with a tactile warning had the shortest mean RT. Drivers with a tactile warning had significantly shorter RT than drivers without a warning and had a significant advantage over drivers with visual warnings. CONCLUSION: Tactile warnings show promise as effective rear-end collision warnings. APPLICATION: The results of this study can be applied to the future design and evaluation of automotive warnings designed to reduce rear-end collisions.     

Identification of driver state for lane-keeping tasks

Identification of driver state is a desirable element of many proposed vehicle active safety systems (e.g., collision detection and avoidance, automated highway, and road departure warning systems). In the paper, driver state assessment is considered in the context of a road departure warning and intervention system. A system identification approach, using vehicle lateral position as the input and steering wheel position as the output, is used to develop a model and to update its parameters during driving. Preliminary driving simulator results indicate that changes in the bandwidth and/or parameters of such a model may be useful indicators of driver fatigue. The approach is then applied to data from 12 2-h highway driving runs conducted in a full-vehicle driving simulator. The identified model parameters (ζ ω_n , and DC gain) do not exhibit the trends expected as lane keeping performance deteriorates, despite having acceptably white residuals. As an alternative, model residuals are compared in a process monitoring approach using a model fit to an early portion of the 2-h driver run. Model residuals show the expected trends and have potential in serving as the basis for a driver state monitor     

Design and evaluation of a model predictive vehicle control algorithm for automated driving using a vehicle traffic simulator

This paper describes the design and evaluation of a model predictive control algorithm for automated driving on a motorway using a vehicle traffic simulator. For the development of a highly automated driving control algorithm, motion planning is necessary to satisfy driving condition in various road traffic situations. There are two key issues in motion planning of automated driving vehicles. One of the key issues is how to handle potentially dangerous situations that could occur in order to guarantee the safety of vehicles. The second key issue is how to guarantee the disturbance rejection of the controller under model uncertainties and external disturbances. To improve safety with respect to the future behaviors of subject vehicles, not the current states but rather the predicted behaviors of surrounding vehicles should be considered. The desired driving mode and a safe driving envelope are determined based on the probabilistic prediction of surrounding vehicles behaviors over a finite prediction horizon. To obtain the desired steering angle and longitudinal acceleration for maintaining the subject vehicle in the safe driving envelope during a finite prediction horizon, a motion planning controller is designed based on an model predictive control (MPC) approach. The developed control algorithm has been successfully implemented on a vehicle electronic control unit (ECU). The proposed control algorithm has been evaluated on a real-time vehicle traffic simulator. The throttle, brake, and steering control inputs and the controlled vehicle behavior have been compared to those of manual driving.     

Fifty years of driving safety research

OBJECTIVE: This brief review covers the 50 years of driving-related research published in Human Factors, its contribution to driving safety, and emerging challenges. BACKGROUND: Many factors affect driving safety, making it difficult to assess the impact of specific factors such as driver age, cell phone distractions, or collision warnings. METHOD: The author considers the research themes associated with the approximately 270 articles on driving published in Human Factors in the past 50 years. RESULTS: To a large extent, current and past research has explored similar themes and concepts. Many articles published in the first 25 years focused on issues such as driver impairment, individual differences, and perceptual limits. Articles published in the past 25 years address similar issues but also point toward vehicle technology that can exacerbate or mitigate the negative effect of these issues. Conceptual and computational models have played an important role in this research. CONCLUSION: Improved crash-worthiness has contributed to substantial improvements in driving safety over the past 50 years, but future improvements will depend on enhancing driver performance and perhaps, more important, improving driver behavior. Developing models to guide this research will become more challenging as new technology enters the vehicle and shifts the focus from driver performance to driver behavior. APPLICATION: Over the past 50 years, Human Factors has accumulated a large base of driving-related research that remains relevant for many of today's design and policy concerns.     

Theoretical and Experimental Investigation of Driver Noncooperative-Game Steering Control Behavior

This paper investigates two noncooperative-game strategies which may be used to represent a human driver's steering control behavior in response to vehicle automated steering intervention.The first strategy,namely the Nash strategy is derived based on the assumption that a Nash equilibrium is reached in a noncooperative game of vehicle path-following control involving a driver and a vehicle automated steering controller.The second one,namely the Stackelberg strategy is derived based on the assumption that a Stackelberg equilibrium is reached in a similar context.A simulation study is performed to study the differences between the two proposed noncooperativegame strategies.An experiment using a fixed-base driving simulator is carried out to measure six test drivers'steering behavior in response to vehicle automated steering intervention.The Nash strategy is then fitted to measured driver steering wheel angles following a model identification procedure.Control weight parameters involved in the Nash strategy are identified.It is found that the proposed Nash strategy with the identified control weights is capable of representing the trend of measured driver steering behavior and vehicle lateral responses.It is also found that the proposed Nash strategy is superior to the classic driver steering control strategy which has widely been used for modeling driver steering control over the past.A discussion on improving automated steering control using the gained knowledge of driver noncooperative-game steering control behavior was made.     

Is it really good to talk? Testing the impact of providing concurrent verbal protocols on driving performance

Abstract

Questions have been raised regarding the impact that providing concurrent verbal protocols has on task performance in various settings; however, there has been little empirical testing of this in road transport. The aim of this study was to examine the impact of providing concurrent verbal protocols on driving performance. Participants drove an instrumented vehicle around a set route, twice whilst providing a concurrent verbal protocol, and twice without. A comparison revealed no differences in behaviour related to speed, braking and steering wheel angle when driving mid-block, but a significant difference in aspects of braking and acceleration at roundabouts. When not providing a verbal protocol, participants were found to brake harder on approach to a roundabout and accelerate more heavily coming out of roundabouts. It is concluded that providing verbal protocols may have a positive effect on braking and accelerating. Practical implications related to driver training and future research are discussed.

Practitioner Summary: Verbal protocol analysis is used by ergonomists to understand aspects of cognition and decision-making during complex tasks such as driving and control room operation. This study examines the impact that it has on driving performance, providing evidence to support its continued use in ergonomics applications.     

Modelling of a Human Driver’s Interaction with Vehicle Automated Steering Using Cooperative Game Theory

The introduction of automated driving systems raised questions about how the human driver interacts with the automated system. Non-cooperative game theory is increasingly used for modelling and understanding such interaction, while its counterpart, cooperative game theory is rarely discussed for similar applications despite it may be potentially more suitable. This paper describes the modelling of a human driver’s steering interaction with an automated steering system using cooperative game theory. The distributed Model Predictive Control approach is adopted to derive the driver’s and the automated steering system’s strategies in a Pareto equilibrium sense, namely their cooperative Pareto steering strategies. Two separate numerical studies are carried out to study the influence of strategy parameters, and the influence of strategy types on the driver’s and the automated system’s steering performance. It is found that when a driver interacts with an automated steering system using a cooperative Pareto steering strategy, the driver can improve his/her performance in following a target path through increasing his/her effort in pursuing his/her own interest under the driver-automation cooperative control goal. It is also found that a driver’s adoption of cooperative Pareto steering strategy leads to a reinforcement in the driver’s steering angle control, compared to the driver’s adoption of non-cooperative Nash strategy. This in turn enables the vehicle to return from a lane-change maneuver to straight-line driving swifter.      

Influence of age and proximity warning devices on collision avoidance in simulated driving

OBJECTIVE: We conducted a set of experiments to examine the utility of several different uni- and multimodal collision avoidance systems (CASs) on driving performance of young and older adult drivers in a high-fidelity simulator. BACKGROUND: Although previous research has examined the efficacy of different CASs on collision avoidance, there has been a dearth of studies that have examined such devices in different driving situations with different populations of drivers. METHOD: Several different CAS warnings were examined in varying traffic and collision configurations both without (Experiment 1a) and with (Experiment 2) a distracting in-vehicle task. RESULTS: Overall, collision avoidance performance for both potential forward and side object collisions was best for an auditory/visual CAS, which alerted drivers using both modalities. Interestingly, older drivers (60-82 years of age) benefited as much as younger drivers from the CAS, and sometimes they benefited more. CONCLUSION: These data suggest that CASs can be beneficial across a number of different driving scenarios, types of collisions, and driver populations. APPLICATION: These results have important implications for the design and implementation of CASs for different driver populations and driving conditions.     

Alarm mistrust in automobiles: how collision alarm reliability affects driving

As roadways become more congested, there is greater potential for automobile accidents and incidents. To improve roadway safety, automobile manufacturers are now designing and incorporating collision avoidance warning systems; yet, there has been little investigation of how the reliability of alarm signals might impact driver performance. We measured driving and alarm reaction performances following alarms of various reliability levels. In Experiment One, 70 participants operated a driving simulator while being presented console emitted collision alarms that were 50%, 75%, or 100% reliable. In Experiment Two, the same participants were presented spatially generated collision alarms of the same reliability levels. The results were similar in both experiments: alarm and automobile swerving reactions were significantly better when alarms were more reliable; however, drivers still failed to avoid collisions following reliable alarms. These results emphasize that alarm designers should maximize alarm reliability while minimizing alarm invasiveness.     

Vision-based active safety system for automatic stopping

Intelligent systems designed to reduce highway fatalities have been widely applied in the automotive sector in the last decade. Of all users of transport systems, pedestrians are the most vulnerable in crashes as they are unprotected. This paper deals with an autonomous intelligent emergency system designed to avoid collisions with pedestrians. The system consists of a fuzzy controller based on the time-to-collision estimate – obtained via a vision-based system – and the wheel-locking probability – obtained via the vehicle’s CAN bus – that generates a safe braking action. The system has been tested in a real car – a convertible Citroën C3 Pluriel – equipped with an automated electro-hydraulic braking system capable of working in parallel with the vehicle’s original braking circuit. The system is used as a last resort in the case that an unexpected pedestrian is in the lane and all the warnings have failed to produce a response from the driver.