A Hybrid Agent-Control System Approach to Analyze Various Driving Behaviors

Abstract

A large percentage of traffic problems occur due to risky or aggressive behavior by drivers (James, 2000). Aggressive driving, incidents of road rage, and following distance are some of the major concerns surrounding the issue, and are increasing in frequency and severity on America's roadways (Rathbone, 1999). An understanding of risky driving and its impact is required in order to help alleviate factors that contribute to these behaviors. Models of risky or aggressive behaviors of drivers allow us to study these effects and their impacts on highway safety, and can further help to identify levers (implicit and explicit) that influence these behaviors. This paper uses a cognitive agent model of human drivers as control systems to aid in the understanding of the effects of heterogeneous beliefs of risk, and the impacts of various driving behaviors on performance.

Keywords

Human behavior modeling intelligent agents cognitive modeling aggressive driving risk assessment control systems

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