Psychological model could make driverless cars more human-friendly
Image credit: University of Leeds
Researchers from the University of Leeds have been investigating how to predict human behaviour – specifically when pedestrians choose to cross the road – in an effort to make driverless cars more friendly to pedestrians.
The researchers set out to determine whether a decision-making model called drift diffusion could predict when pedestrians would cross a road in front of approaching cars and whether it could be used in scenarios where the car gives way to the pedestrian, either with or without explicit signals.
This would in turn allow the autonomous vehicle to communicate more effectively with pedestrians – in terms of its movements in traffic and any external signals such as flashing lights – to maximise traffic flow and decrease uncertainty.
Drift diffusion models assume that people reach decisions after accumulation of sensory evidence up to a threshold at which the decision is made.
“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,” said Professor Gustav Markkula, senior author of the study.
“When a vehicle is giving way, pedestrians will often feel quite uncertain about whether the car is actually yielding, and will often end up waiting until the car has almost come to a full stop before starting to cross. Our model clearly shows this state of uncertainty borne out, meaning it can be used to help design how automated vehicles behave around pedestrians in order to limit uncertainty, which in turn can improve both traffic safety and traffic flow. It is exciting to see that these theories from cognitive neuroscience can be brought into this type of real-world context and find an applied use.”
To test their model, the team used a VR headset to place trial participants in different road-crossing scenarios in the University of Leeds’ unique “Highly Immersive Kinematic Experimental Research” pedestrian simulator. Their movements were tracked in detail while walking freely inside a stereoscopic 3D virtual scene which depicted a road with oncoming vehicles; they simply had to cross the road when they felt safe to do so.
Different scenarios were tested, with the approaching vehicle either maintaining the same speed or decelerating to let the pedestrian cross, sometimes also flashing the headlights, representing a commonly used signal for yielding intentions in the UK.
As predicted by their model, the researchers found that participants behaved as if they were deciding on when to cross by combining the sensory data from vehicle distance, speed, acceleration, as well as communicative cues. This meant that their drift diffusion model could predict if, and when, pedestrians would be likely to begin crossing the road.
Markkula explained: “These findings can help provide a better understanding of human behaviour in traffic, which is needed both to improve traffic safety and to develop automated vehicles that can coexist with human road users. Safe and human-acceptable interaction with pedestrians is a major challenge for developers of automated vehicles, and a better understanding of how pedestrians behave will be key to enable this.”
Dr Jami Pekkanen, who led the research while at the University of Leeds, commented: “Predicting pedestrian decisions and uncertainty can be used to optimise when, and how, the vehicle should decelerate and signal to communicate that it's safe to cross, saving time and effort for both."
Sign up to the E&T News e-mail to get great stories like this delivered to your inbox every day.