Cognitive Architectures for Modeling Driver Behavior

2011 ◽  
Author(s):  
Dario Salvucci
Keyword(s):  
Driver Behavior ◽  
Cognitive Architectures

Toward an Integrated Model of Driver Behavior in Cognitive Architecture

2001 ◽  
Vol 1779 (1) ◽  
pp. 9-16 ◽  
Author(s):  
Dario D. Salvucci ◽  
Erwin R. Boer ◽  
Andrew Liu
Keyword(s):  
Driving Simulator ◽  
Driver Behavior ◽  
Cognitive Architecture ◽  
Driver Model ◽  
Behavioral Variability ◽  
Level Control ◽  
Cognitive Architectures ◽  
Computational Framework ◽  
Lane Changes ◽  
Motor Processes

Driving is a multitasking activity that requires drivers to manage their attention among various driving- and non-driving-related tasks. When one models drivers as continuous controllers, the discrete nature of drivers’ control actions is lost and with it an important component for characterizing behavioral variability. A proposal is made for 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 multitasking activity. These benefits are demonstrated 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 a 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, and higher-level cognitive tasks, such as task management and decision making. Many of these predictions are not explicitly built into the model but come from the cognitive architecture as a result of the model’s implementation in the ACT-R architecture.

Passenger Influence on Driver Behavior

2006 ◽  
Author(s):  
Hilary M. Anton-Stang ◽  
Sharan E. Gay ◽  
Krista B. Kumley ◽  
Tressa Mallomo ◽  
Stace Nichols ◽  
...  
Keyword(s):  
Driver Behavior

Impulsive Driver Behavior Scale--Chinese Version

2020 ◽  
Author(s):  
Yan Ge ◽  
Weina Qu ◽  
Mu Zhou ◽  
Türker Özkan ◽  
Pınar Bıçaksız ◽  
...  
Keyword(s):  
Driver Behavior ◽  
Chinese Version ◽  
Behavior Scale

Capabilities

2018 ◽  
Author(s):  
Paul F. M. J. Verschure
Keyword(s):  
Building Blocks ◽  
Living Systems ◽  
System Level ◽  
Cognitive Architectures ◽  
Multiple Components ◽  
Perception Action

This chapter introduces the “Capabilities” section of the Handbook of Living Machines. Where the previous section considered building blocks, we recognize that components or modules do not automatically make systems. Hence, in the remainder of this handbook, the emphasis is toward the capabilities of living systems and their emulation in artifacts. Capabilities often arise from the integration of multiple components and thus sensitize us to the need to develop a system-level perspective on living machines. Here we summarize and consider the 14 contributions in this section which cover perception, action, cognition, communication, and emotion, and the integration of these through cognitive architectures into systems that can emulate the full gamut of integrated behaviors seen in animals including, potentially, our own capacity for consciousness.

Understanding Driver Behavior after Concussion: A Machine-Learning Approach

2020 ◽  
Vol 64 (1) ◽  
pp. 1911-1915
Author(s):  
Maryam Daniali ◽  
Dario D. Salvucci ◽  
Maria T. Schultheis
Keyword(s):  
Machine Learning ◽  
Task Performance ◽  
Verbal Memory ◽  
Driving Simulator ◽  
Cognitive Impairments ◽  
Driver Behavior ◽  
Memory Task ◽  
Learning Approach ◽  
Behavioral Task ◽  
Machine Learning Approach

Concussions are common cognitive impairments, but their effects on task performance in general, and on driving in particular, are not well understood. To better understand the effects of concussion on driving, we investigated previously gathered data on twenty-two people with a concussion, driving in a virtual-reality driving simulator (VRDS), and twenty-two non-concussed matched drivers. Participants were asked to per-form a behavioral task (either coin sorting or a verbal memory task) while driving. In this study, we chose a few common metrics from the VRDS and tracked their changes through time for each participant. Our pro-posed method—namely, the use of convolutional neural networks for classification and analysis—can accu-rately classify concussed driving and extract local features on driving sequences that translate to behavioral driving signatures. Overall, our method improves identification and understanding of clinically relevant driv-ing behaviors for concussed individuals and should generalize well to other types of impairments.

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