The Use of ACT-R to Develop an Attention Model for Simple Driving Tasks (original) (raw)
Related papers
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.
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.
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.
Neuro-ACT Cognitive Architecture Applications in Modeling Driver's Steering Behavior in Turns
2015
Cognitive Architectures (CAs) are the core of artificial cognitive systems. A CA is supposed to specify the human brain at a level of abstraction suitable for explaining how it achieves the functions of the mind. Over the years a number of distinct CAs have been proposed by different authors and their limitations and potentials were investigated. These CAs are usually classified as symbolic and sub-symbolic architectures. In this work, a novel hybrid architecture is proposed that encompasses a symbolic part (i.e. ACT-R) to explain the controlled aspects of behavior and a sub-symbolic part (i.e. Artificial Neural Networks) to describe automated skills. In order to demonstrate the capabilities of the proposed model, an experiment was conducted in which, a rather complex real life task was carried out by the model and its result were compared with those of human participants. Simulation results have shown promising capabilities of the new architecture in modeling complex human behavior.
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
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
Toward adaptive support : Modelling drivers' allocation of attention
2002
Driver distraction and inattention are major contributing factors in traffic accidents (cf., e.g., Najm et al, 1995). Some of these accidents might be avoided in the future if drivers' (miss)allocation of attention could be detected, and the driver be prompted toward key events in the traffic scene. Our objective is to develop a cognitively based driver model where drivers' allocation of attention can be simulated for diagnostic purposes. As a first step, we present a connectionist model framework implemented in PDP++. This framework is based on the notion that focusing attention on a visual object is closely coupled with an intention to act on that object. Using our model, we want to continuously assess if the driver can maintain a general preparedness to act, and hence detect unexpected events, or if he/she is overloaded by non-driving-related tasks. In a first step, we intend to study how attention can be allocated to subtasks in the model, to find out to what extent multiple tasks can be parallelized by drivers. For further developments on this project, visit http://www.ida.liu.se/\~ritko.html.
Development of an Empirical Model to Assess Attention Level and Control Driver's Attention
International Journal of Computer Science, Engineering and Information Technology, 2018
Any kind of vehicle driving is one of the most challenging tasks in this world requiring simultaneous accomplishment of numerous sensory, cognitive, physical and psychomotor skills. There are various number of factors are involved in automobile crash such as driver skill, behaviour and impairment due to drugs, road design, vehicle design, speed of operation, road environment, notably speeding and street racing. This study focuses a vision based framework to monitor driver's attention level in real time by using Microsoft Kinect for Windows sensor V2. Additionally, the framework generates an awareness signal to the driver in case of low attention. The effectiveness of the system demonstrates through board experiments in case of hostile light conditions also. Experimental result illustrates the quite well functionality of the framework with 11 participants and measures the attention level of participants with equitable precision.
Proceedings of the Human Factors and Ergonomics Society Annual Meeting
The goal of this research is to computationally model and simulate drivers’ situation awareness (SA). In order to achieve this, we have developed a computational cognitive model in a cognitive architecture that can be connected to interact with a driving simulator, as means to infer quantitative predictions of drivers’ SA. We demonstrate the theory of modelling and predicting SA through the lens of human cognition utilizing the QN-ACTR (Queueing Network-Adaptive Control of Thought-Rational) framework as a foundation. We integrate a dynamic visual sampling model (SEEV) to create QN-ACTR-SA in order to allow the model to simulate realistic attention allocation patterns of human drivers. A driver model is also incorporated within QN-ACTR-SA architecture that can simulate human driving behavior by interacting with a driving simulator with the help of virtual modalities such as motor, visual and memory functions. A preliminary validation study is conducted to determine whether SA results...
A driver visual attention model
1997
In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make jt freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It is understood that copying or publication. of this thesis for financial gain shall not be allowed without my written permission.