Self-driving supermarkets and autonomous electric taxis capable of slashing emissions in New York are some of the innovations driving the driverless revolution.
The issue of safety is the recurring question though, but thanks to new research it will now be possible for automated vehicles to tap into the way our brains work to predict when someone is likely to step out from the kerbside in busy traffic.
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.
Professor Gustav Markkula
Scientists at the University of Leeds set out to discover if neuroscientific theories on decision making in humans could be integrated into automated vehicle technology. They looked at a decision-making model called drift diffusion to determine if the tech could detect when a person was about to cross the road. In trials at the university’s HIKER (Highly Immersive Kinematic Experimental Research) pedestrian simulator, the team tested how people respond in typical scenarios where a car gives way, such as when a car begins to slow down, or flashes its lights, which in turn can inform the vehicle technology, allowing for better communication between car and pedestrian.
“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, from the University of Leeds’ Institute for Transport Studies and the 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.”
Inside HIKER the participants were told to cross the road when they felt safe to do so, and as the model predicted, they made decisions based on the different sources of evidence available to them, meaning the drift diffusion model was able to predict when a person was likely to cross.
Professor Markkula added: “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.”
