Toward an Integrated Model of Driver Behavior in Cognitive Architecture

Abstract

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.

A game theoretical model of traffic with multiple interacting drivers for use in autonomous vehicle development

“…Many of the proposed models lack driver interaction dynamics, which are important for operating in traffic and simulating real driving. One example of a model that incorporates interactions between drivers can be found in [5], where built in logical rules (if-then-else) are used to represent the decision making process. Although this approach successfully incorporates multi-tasking behavior and interaction of human drivers, it is not clear how the logical rules are obtained, and furthermore, the dynamic behavior (multi step decision making) is not considered.…”

An algorithm for combining autonomous vehicles and controlled events in driving simulator experiments

“…However, the car-following and lane changing models used in Janson Olstam et al (2008) have been replaced by versions of the HDM/IDM car-following model (Treiber et al, 2000(Treiber et al, , 2006 and a combination of the ARCHISIM (El Hadouaj and Espié, 2002) and the MOBIL (Kesting et al, 2007) lane-changing models. The lateral position update algorithm has been replaced by the steering model presented in Salvucci et al (2001). The simulation framework has been integrated with and tested in the VTI driving simulator III (VTI, 2008).…”

Cognitive Coordination on the Network Centric Battlefield

“…) in press; Cassimatis 2005; Cassimatis et al 2004;Lee & Anderson 2001;Salvucci et al 2001;Schoelles & Gray 2000;Scolaro & Santarelli 2002). Among the cognitive architectures being used to build complex cognitive systems are ACT-R Anderson & Lebiere 1998), SOAR (Newell 1990;Laird et al 1987), EPIC (Kieras & Meyer 1997), Polyscheme (Cassimatis 2002), COGENT (Cooper 2002), ICARUS (Langley in press), CogNet (Zachary & Ross 1991) and CLARION (Sun 2005).…”