Autonomous cars that are trained to respond more like humans to danger will cause fewer injuries during road accidents, according to a study that shows how driverless vehicles might be made safer. Vulnerable groups such as cyclists, pedestrians and motorcyclists saw the biggest gains in protection when driverless cars used “social sensitivity” in assessing the collective impact of multiple hazards. The study, published in the US Proceedings of the National Academy of Sciences, highlights growing efforts to balance AVs’ efficient operation with the need for them to minimise damage in collisions. The research comes as leading tech companies such as Tesla, Google’s Waymo and Amazon’s Zoox push to roll out AVs — which use a range of sensors and automated software to drive without human intervention — around the world. Manufacturers must train AVs to respond instantly to real-world dilemmas, such as what to collide with if a crash becomes unavoidable. The issue of AV ethics is attracting increasing attention as growing use of the cars offers the prospect of eliminating driver problems such as spatial misjudgments and fatigue. The study suggests human behavioural methods could “provide an effective scaffold for AVs to address future ethical challenges”, said its China- and US-based authors, led by Hongliang Lu of The Hong Kong University of Science and Technology.

 Full study : Autonomous vehicles trained to use “social sensitivity” in assessing the collective impact of multiple hazards cause fewer injuries during road accidents.