A team of British researchers has developed a new model that could make automated vehicle more pedestrian-friendly by enhancing their ability to predict people's behaviors.
University of Leeds scientists have investigated how to use the brain's decision-making mechanism in improving the safety of automated vehicle technology when dealing with pedestrians.
The researchers created a new model they called "drift diffusion" to enable an automated vehicle to predict when pedestrians would cross a road with the help of different scenarios aimed at reducing accidents risks.
According to the Phys. org website, this model will allow the autonomous vehicle to communicate more effectively with pedestrians through the car's flashing lights for instance.
The drift diffusion models assume that people reach the decision to cross the road or not after accumulation of sensory evidence.
Professor Gustav Markkula, from the University of Leeds' Institute for Transport Studies and the senior author of the study, said: "When making the decision to cross, pedestrians seem to be adding up lots of different sources of evidence, not only relating to the vehicle's distance and speed, but also using communicative cues from the vehicle in terms of deceleration and headlight flashes."
To test their model, the team used virtual reality to place trial participants in different road-crossing scenarios in different environments.
The participants' task was to cross the road as soon as they felt safe to do so. Professor Markkula said: "These findings can help provide a better understanding of human behavior in traffic, which is needed both to improve traffic safety and to develop automated vehicles that can coexist with human road users."