Robotic vehicles have been used in dangerous environments for decades, from decommissioning the Fukushima nuclear power plant or inspecting underwater energy infrastructure in the North Sea. More recently, autonomous vehicles from boats to grocery delivery carts have made the gentle transition from research centers into the real world with very few hiccups.
Yet the promised arrival of self-driving cars has not progressed beyond the testing stage. And in one test drive of an Uber self-driving car in 2018, a pedestrian was killed by the vehicle. Although these accidents happen every day when humans are behind the wheel, the public holds driverless cars to far higher safety standards, interpreting one-off accidents as proof that these vehicles are too unsafe to unleash on public roads.
Programming the perfect self-driving car that will always make the safest decision is a huge and technical task. Unlike other autonomous vehicles, which are generally rolled out in tightly controlled environments, self-driving cars must function in the endlessly unpredictable road network, rapidly processing many complex variables to remain safe.
Inspired by the highway code, researchers are working on a set of rules that will help self-driving cars make the safest decisions in every conceivable scenario. Verifying that these rules work is the final roadblock we must overcome to get trustworthy self-driving cars safely onto our roads.
Asimov’s first law
Science fiction author Isaac Asimov penned the “three laws of robotics” in 1942. The first and most important law reads: “A robot may not injure a human being or, through inaction, allow a human being to come to harm.” When self-driving cars injure humans, they clearly violate this first law.
Researchers at the National Robotarium are leading research intended to guarantee that self-driving vehicles will always make decisions that abide by this law. Such a guarantee would provide the solution to the very serious safety concerns that are preventing self-driving cars from taking off worldwide.