Studies on human behaviour can be used in making automated cars safer: Report

A new research published in the Computational Brain amp Behavior Journal has found how automated vehicles could be made more pedestrianfriendlyUniversity of Leedsled scientists investigating how to better understand human behaviour in traffic said that neuroscientific theories of how the brain makes decisions can be used in automated vehicle technology to improve safety and make them more humanfriendly The researchers set out to determine whether a decisionmaking model called driftdiffusion 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 signalsThis prediction capability will 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 assumed that people reach decisions after the accumulation of sensory evidence up to a threshold at which the decision is madeProfessor 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 vehicles distance and speed but also using communicative cues from the vehicle in terms of deceleration and headlight flashesWhen 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 realworld context and find an applied use he addedTo test their model the team used virtual reality to place trial participants in different roadcrossing scenarios in the University of Leeds unique HIKER Highly Immersive Kinematic Experimental Research pedestrian simulator Study participants movements were tracked in high detail while walking freely inside a stereoscopic 3D virtual scene showing a road with oncoming vehicles The participants task was to cross the road as soon as they felt safe to do soDifferent 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 adding up over time the sensory data from vehicle distance speed acceleration as well as communicative cues This meant that their driftdiffusion model could predict if and when pedestrians would be likely to begin crossing the roadProfessor Markkula said 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 humanacceptable 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 thisLead author Dr Jami Pekkanen who carried out the research while at the University of Leeds said Predicting pedestrian decisions and uncertainty can be used to optimise when and how the vehicle should decelerate and signal to communicate that its safe to cross saving time and effort for bothAlso Read Yearender 2021 What did Mumbairsquos brave new restaurants learn in their first tough year in business