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U researchers John Hourdos and Wuping Xin are developing a new driving simulation model that's capable of replicating real-life car-following behaviors with all their risks and imperfections.
Helping the less-than-perfect driver
By Peter Park Nelson
From eNews, March 22, 2007
Imagine a world where drivers don't make mistakes. Everyone pays perfect attention to the movements of cars around them, and no one takes unnecessary risks like following too closely behind another vehicle. This utopian world really exists--at least in research labs where computer simulations model the movements of virtual vehicles.
But like all utopias, these neat and tidy models are not as perfect as they appear.
For University of Minnesota civil engineering professor Panos Michalopoulos and traffic researcher John Hourdos--who are trying to understand how and why vehicle crashes happen and how crashes affect traffic--the behavior of virtual vehicles is frustratingly limited. Joined by civil engineering professor Gary Davis and graduate student Wuping Xin, the researchers set out to develop a more accurate and complete model of car-following behavior. The U's Intelligent Transportation Systems Institute funded the study.
Reflecting reality The first attempts to mathematically describe the behavior of vehicles under simple traffic conditions were made in the 1950s, by scientists and engineers seeking to understand how disturbances in traffic flow propagate down a line of moving vehicles.
A significant advance occurred with the development of psycho-physical models that more accurately reflect the decision-making processes of drivers. Whereas in earlier models every vehicle adjusted its speed constantly based on distance to the vehicle ahead, vehicles in a psycho-physical model change their acceleration only when they reach an "action point"--for example, when the distance to a vehicle ahead drops below a specified distance. Today, this principle is incorporated into the car-following models in several widely used commercial simulation systems.
Yet, despite the success of current car-following models at reproducing many observed features of traffic flow, existing approaches fail to capture the intricacies of individual driver behavior, such as reaction time delays, distraction and errors in judgment.
To understand the genesis of dangerous traffic conditions, many researchers look for evidence of instability within the equation systems that govern car-following models. These points of instability--where the models "break down" and the virtual vehicles begin to collide--are indicators of accident-prone traffic conditions.
One example is the formation of high-density "traffic waves" in which gaps between vehicles become too short for drivers to avoid rear-end collisions. This phenomenon can arise naturally from the dynamic interaction between vehicles as traffic density increases. Another example relates to driver performance. Factors such as visual perception and decision errors appear to exert a significant influence on the car-following behavior of individual vehicles.
If traffic simulation is to provide a clear picture of factors that cause crashes, car-following models must take both these sources of instability into account.
Model behavior Wuping Xin has spent a lot of time thinking about instability. Under the direction of Micholopoulos and Hourdos, Xin took on the challenge of implementing the research team's conceptual model of driver behavior and vehicle response into computer software that can interact with standard simulation packages used by traffic researchers.
The new car-following model offers a more realistic simulation of the driver's perception-response process because it varies according to external conditions. The simulation relies on certain perceptual cues to determine when the driver is approaching too closely to the vehicle ahead, and then Xin's software makes acceleration and braking decisions by analyzing those perceptual cues.
The U project is ongoing, capitalizing on detailed car-following data collected in Germany, Japan and on the Twin Cities freeways. The research team plans to improve its accuracy as a powerful tool for examining collisions by adding new features, such as a multiple vehicle detector, which would more closely mimics the behavior of human drivers who frequently respond to the braking or acceleration of cars further ahead in the lane.