Toward adaptive support : Modelling drivers' allocation of attention (original) (raw)
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A Cognitive Model of Drivers Attention
PsycEXTRA Dataset, 2000
Cognitive architectures can account for highly complex tasks. One of the greatest challenges is understanding and modeling human driving behavior. This paper describes an integrated cognitive model of human attention during the performance of car driving. In this task, the attention process can be divided into at least three basic components: the control process, the monitoring process, and finally, the decision making process. Of these basic tasks, the first has the highest priority. All three phases are implemented in a cognitive model in the cognitive Architecture ACT-R 6.0. The model is able to keep a traffic lane, overtake another vehicle by lane change, identifies traffic signs and different situations emerging at crossroads.
2014
Simulating and predicting behaviour of human drivers with Digital Human Driver Models (DHDMs) has the potential to support designers of new (partially autonomous) driver assistance systems (PADAS) in early stages with regard to understanding how assistance systems affect human driving behaviour. This paper presents the current research on an integrated driver model under development at OFFIS within the EU project ISi-PADAS 2. We will briefly show how we integrate improvements into CASCaS, a cognitive architecture used as framework for the different partial models which form the integrated driver model. Current research on the driver model concentrates on two aspects of longitudinal control (behaviour a signalized intersections and allocation of visual attention during car following). Each aspect is covered by a dedicated experimental scenario. We show how experimental results guide the modelling process. 1
Toward an Integrated Model of Driver Behavior in Cognitive Architecture
Transportation Research Record, 2001
Driving is a multitasking activity that requires drivers to manage their attention between various driving-and non-driving-related tasks. By modeling drivers as continuous controllers, the discrete nature of drivers' control actions is lost and with it an important component for characterizing behavioral variability. We propose the use of cognitive architectures for developing models of driver behavior that integrate cognitive and perceptual-motor processes in a serial model of task and attention management. A cognitive architecture is a computational framework that incorporates built-in, well-tested parameters and constraints on cognitive and perceptual-motor processes. All driver models implemented in a cognitive architecture necessarily inherit these parameters and constraints, resulting in more predictive and psychologically plausible models than those that do not characterize driving as a multi-tasking activity. We demonstrate these benefits with a driver model developed in the ACT-R cognitive architecture. The model is validated by comparing its behavior to that of human drivers navigating a four-lane highway with traffic in our fixed-based driving simulator. Results show that the model successfully predicts aspects of both lower-level control, such as steering and eye movements during lane changes, as well as higher-level cognitive tasks, such as task management and decision making. Many of these predictions are not explicitly built into the model but rather come from the cognitive architecture as a result of the model's implementation in the ACT-R architecture.
Computational Simulation of Visual Distraction Effects on Car Drivers' Situation Awareness
PsycEXTRA Dataset, 2000
This paper presents a computational modeling approach for negative effects simulation of visual distraction while driving a car. In order to investigate these effects, an experiment was firstly implemented on a driving simulator. Twenty participants were invited to perform a car following task in different driving conditions (12 driving scenarios), with or without a secondary task of visual distraction. Empirical data collected through this experiment show that visual distraction negatively impacts the driving performance at both perceptive and behavioral levels, and then increase the risk of having a crash. Beyond these effects on the observable performance, the aim of this study is also to investigate and simulate these distractive effects on mental models of the road environment. Indeed, driver's decisions and behaviors are based on a temporal-spatial mental model, corresponding to the driver's situation awareness (SA). This mental representation must be permanently updated by perceptive information extracted from the road scene to be efficient. In case of visual distraction requiring off-road scanning, mental model updating is imperfectly done and driver's actions are thus based on a mental representation that can dramatically differ from the situational reality, in case of a critical change in the traffic conditions (e.g. sudden braking of the lead car). From these empirical results, a computational model (named COSMODRIVE for COgnitive Simulation MOdel of the DRIVEr) was implemented for simulating visual distraction effects and human errors risks at perceptive (visual scanning changes) cognitive (erroneous Situation Awareness) and behavioral levels (late reaction time and crash risk increasing).
The aim of this research is to develop and implement a computational model able to simulate drivers' visual strategies in dual-task conditions and to investigate visual distraction effects. The modeling approach supporting this research is based on a cognitive model of the car driver so-called COSMODRIVE, focused on mental representations simulation (i.e. situational awareness) and implemented on a virtual platform (so-called SiVIC). In this framework, a module of the visual perception is needed for model interaction with the virtual road environment. This perception module is indeed the ''entry point'' for road scene analysis and decision-making. This modeling work is based on empirical data collected among 20 human drivers. Experiments have been designed in order to study visual distraction impact of driver's visual scanning. During this experiment, participants drove a car on simulator and answered at the same time to a visual secondary task. Visual strate...
SPIDER A Framework for Understanding Driver Distraction
Objective: The objective was to identify key cog-nitive processes that are impaired when drivers divert attention from driving. Background: Driver distraction is increasingly recognized as a significant source of injuries and fatalities on the roadway. Method/Results: A " SPIDER " model is developed that identifies key cognitive processes that are impaired when drivers divert attention from driving. SPIDER is an acronym standing for scanning, predicting, identifying, decision making, and executing a response. Conclusion: When drivers engage in secondary activities unrelated to the task of driving, SPIDER-related processes are impaired, situation awareness is degraded, and the ability to safely operate a motor vehicle may be compromised. Application: The pattern of interference helps to illuminate the sources of driver distraction and may help guide the integration of new technology into the automobile.
IEEE Transactions on Intelligent Transportation Systems, 2013
Driver distraction detection and intervention are important for designing modern driver-assistance systems and for improving safety. The main research question of this paper is to investigate how the cumulative driver off-road glance duration can be controlled to reduce the probability of occurrences of crash and near-crash events. Based on the available data sets from the Virginia Tech Transportation Institute (VTTI) 100-car study, the conditional probability is calculated to study the chance of crash and near-crash events when the given cumulative off-road glance duration in 6 s has been reached. Different off-road eye-glance locations and traffic density levels are also evaluated. The results show that one linear relationship can be obtained between the cumulative off-road eye-glance duration in 6 s and the risk of occurrences of crash and near-crash events, which varies for different off-road eye-glance locations. In addition, the traffic density level is found to be one significant moderator to this linear relationship. Detailed comparisons are made for different traffic density levels, and one nonlinear equation is obtained to predict the probability of occurrences of crash and near-crash events by considering both cumulative off-road glance duration and traffic density levels.
Multitasking Driver Cognitive Behavior Modeling
2006
In order to process multitasking driver behavior effectively, an improved driver cognitive behavior modeling method of ACT-R is proposed in this paper. The manual module and visual module of ACT-R are concatenated directly to cope with human subconscious/unconscious behavior. A parallel processing method is proposed to mimic the parallel reactions style of a given cerebral area of human brain's reaction to the physical characteristics of the stimulus. Drive behavior assorting and risk level ranking method are applied to improve the model's executive efficiency. The results of the software simulation show that the improvements of the ACT-R cognitive architecture are efficient and flexible
Driver Modeling for Detection & Assessment of Distraction : Examples from the UTDrive testbed
2017
Vehicle technologies have advanced significantly over the past twenty years, especially with respect to novel in-vehicle systems for route navigation, information access, infotainment, and connected vehicle advancements for vehicle-to-vehicle and vehicle-to-infrastructure connectivity/communications. While there is great interest in migrating to fully automated/self-driving vehicles, a number of factors such as: technology performance and cost barriers, interest in public safety, insurance issues, legal implications, government regulations, etc. all suggest it is more likely to have multi-functional vehicles, which allow for smooth transitions from complete human control towards semi-supervised/assisted, to fully automated. In this regard, next generation vehicles will need to be more active in assessing driver awareness, vehicle capabilities, traffic/environmental settings, and how these come together to determine a collaborative safe and effective driver-vehicle engagement for veh...
URGENT DRIVER BEHAVIOR MODELING IN COGNITIVE ARCHITECTURE
The paper construct the driver behavior model in cognitive architecture of the urgent circumstance based on ACT-R theory, it strive to delineate the cognitive procedure of the driver's motor under urgent situation, and find the key cognitive factors of traffic accident avoidance, and then applying the model to the driver's training and instruction, and in the end the modeling method comes truth of decreasing traffic accident. The paper retrospect the research trips of traffic safety and driver behavior firstly, it is aim to point out that the cognitive science is the essential theory for problem-solving of traffic safety and that driver behavior modeling becoming one of the hottest research spot at present is necessary. On the basis of comparison of different typical cognitive architecture and analysis of the theories and researches of ACT-R, A driver behavior modeling method based on ACT-R is raised. A driver behavior model in ACT-R under the urgent circumstance at a hard braking of the preceding vehicle's driver is presented in this paper, and the methods which based on both the prediction of driver behavior and verification of cognitive model are proofed to be widely suitable and flexible. Finally, the illustration of the benefits of drive behavior modeling in cognitive architecture make it an assertion that the application of driver behavior model in the field of traffic safety will be set up effectively and take effects greatly.