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 centres 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, we’re 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.
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.
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Are self-driving cars safe? Expert on how we will drive in the future
We 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.
AI software is actually quite good at learning about scenarios it has never faced. Using “neural networks” that take their inspiration from the layout of the human brain, such software can spot patterns in data, like the movements of cars and pedestrians, and then recall these patterns in novel scenarios.
But we still need to prove that any safety rules taught to self-driving cars will work in these new scenarios. To do this, we can turn to formal verification: the method that computer scientists use to prove that a rule works in all circumstances.
In mathematics, for example, rules can prove that x + y is equal to y + x without testing every possible value of x and y. Formal verification does something similar: it allows us to prove how AI software will react to different scenarios without our having to exhaustively test every scenario that could occur on public roads.
One of the more notable recent successes in the field is the verification of an AI system that uses neural networks to avoid collisions between autonomous aircraft. Researchers have successfully formally verified that the system will always respond correctly, regardless of the horizontal and vertical manoeuvres of the aircraft involved.
Human drivers follow a highway code to keep all road users safe, which relies on the human brain to learn these rules and applying them sensibly in innumerable real-world scenarios. We can teach self-driving cars the highway code too. That requires us to unpick each rule in the code, teach vehicles’ neural networks to understand how to obey each rule, and then verify that they can be relied upon to safely obey these rules in all circumstances.
However, the challenge of verifying that these rules will be safely followed is complicated when examining the consequences of the phrase “must never” in the highway code. To make a self-driving car as reactive as a human driver in any given scenario, we must program these policies in such a way that accounts for nuance, weighted risk and the occasional scenario where different rules are in direct conflict, requiring the car to ignore one or more of them.
Robot ethicist Patrick Lin introducing the complexity of automated decision-making in self-driving cars.
Such a task cannot be left solely to programmers – it’ll require input from lawyers, security experts, system engineers and policymakers. Within our newly formed AISEC project, a team of researchers is designing a tool to facilitate the kind of interdisciplinary collaboration needed to create ethical and legal standards for self-driving cars.
Teaching self-driving cars to be perfect will be a dynamic process: dependent upon how legal, cultural and technological experts define perfection over time. The AISEC tool is being built with this in mind, offering a “mission control panel” to monitor, supplement and adapt the most successful rules governing self-driving cars, which will then be made available to the industry.
We’re hoping to deliver the first experimental prototype of the AISEC tool by 2024. But we still need to create adaptive verification methods to address remaining safety and security concerns, and these will likely take years to build and embed into self-driving cars.
Accidents involving self-driving cars always create headlines. A self-driving car that recognises a pedestrian and stops before hitting them 99% of the time is a cause for celebration in research labs, but a killing machine in the real world. By creating robust, verifiable safety rules for self-driving cars, we’re attempting to make that 1% of accidents a thing of the past.
e.komendantskaya@hw.ac.uk receives funding from EPSRC, NCSC, DSTL.
Luca Arnaboldi and Matthew Daggitt do not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.
Original post published in The Conversation.
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For my PhD, I’m studying how global problems such as wildfires and aid delivery in remote areas can benefit from innovative technologies such as UAV (unmanned aerial vehicle) swarms.
Every year, vast areas of forests are destroyed due to wildfires. Wildfires occur more frequently as climate change induces extreme weather conditions. As a result, wildfires are often larger and more intense. Over the past 5 years, countries around the globe witnessed unprecedented effects of wildfires. Greece has seen the deadliest wildfire incident in its modern history, Australia witnessed 18,636,079 hectares being burnt and British Columbia faced wildfire incidents that burnt 1,351,314 hectares.
Rather than jump straight in to the design of swarm algorithms for UAVs in these scenarios, I spent a year speaking with firefighters around the world to ask about their current operations and how UAVs could help. This technique is called mutual shaping where end users cooperate with developers to co-create a system. It’s been interesting to see the challenges they face, their openness to drones and their ideas on the operation of a swarm. Firefighters face numerous challenges in their operations. Their work is dangerous and they need to ensure the safety of citizens and themselves. However, they often don’t have enough information about the environment they are deploying in. The good news is that UAVs are already used by firefighters to retrieve information during their operations, usually during very short human-piloted flights with small off-the-shelf drones. Having larger drones, with longer autonomy and higher payload, could provide high-value information to firefighters or actively identify and extinguish newly developed fire fronts.
I think one of the reasons we don’t have these swarms in these applications is that 1) we didn’t have the hardware capabilities (N robots with high-payload at a reasonable cost) , 2) we don’t have the algorithms that allow for effective swarm deployments at the scale of a country (necessary to monitor for forest fires), and 3) we can’t easily change our swarm strategies on the go based on what is happening in reality. That’s where digital twins come in. A digital twin is the real-time virtual representation of the world we can use to iterate solutions. The twin can be simplistic in 2D or a more realistic 3D representation, with data from the real-world continuously updating the model.
To develop this framework for digital twins we’re starting a new project with Windracers ltd, Distributed Avionics ltd, and the University of Bristol co-funded by Innovate UK. Windracers ltd. has developed a novel UAV: the ULTRA platform, that can transport 100kg of payload over a 1000km range. Distributed Avionics specialises in high-reliability flight control solutions and Hauert Lab, which engineers swarm systems across scales – from huge number of tiny nanoparticles for cancer treatment to robots for logistics.
In the future, we aim to use the same concepts to enable aid delivery using UAV swarms. The 2020 Global Report on Food Crises states that more than 135 million people across 53 countries require urgent food, nutrition, and livelihoods assistance. Aerial delivery of such aid could help access remote communities and avoid in-person transmission of highly infectious diseases such as COVID-19.
Here’s a video presenting the concept:
By the end of this project, we are hoping to demonstrate the deployment of 5 real UAVs that will be able to interact with a simple digital twin. We also want to test live deployment of the control swarm system on the UAVs.
Are you a firefighter, someone involved in aid delivery, do you have other ideas for us. We’d love to hear from you.
The climate emergency brooks no compromise: every human activity or artefact is either part of the solution or it is part of the problem.
I’ve worried about the sustainability of consumer electronics for some time, and, more recently, the shocking energy costs of big AI. But the climate emergency has also caused me to think hard about the sustainability of robots. In recent papers we have defined responsible robotics as
the application of Responsible Innovation in the design, manufacture, operation, repair and end-of-life recycling of robots, that seeks the most benefit to society and the least harm to the environment.
I will wager that few robotics manufacturers – even the most responsible – pay much attention to repairability and recyclability of their robots. And, I’m ashamed to say, very little robotics research is focused on the development of sustainable robots. A search on google scholar throws up a handful of excellent papers detailing work on upcycled and sustainable robots (2018), sustainable robotics for smart cities (2018), green marketing of sustainable robots (2019), and sustainable soft robots (2020).
I was therefore delighted when, a few weeks ago, my friend and colleague Michael Fisher, drafted a proposal for a new standard on Sustainable Robotics. The proposal received strong support from the BSI robotics committee. Here is the formal notice requesting comments on Michael’s proposal: BS XXXX Guide to the Sustainable Design and Application of Robotic Systems.
So what would make a robot sustainable? In my view it would have to be:
These are, for me, the four fundamental requirements, but there are others. The BSI proposal adds also the environmental effects of deployment (it is unlikely we would consider a sustainable robot designed to spray pesticides as truly sustainable), or of failure in the field. Also the environmental effect of maintenance; cleaning materials, for instance. The proposal also looks toward sustainable, upcyclable robots as part of a circular economy.
This is Ecobot III, developed some years ago by colleagues in the Bristol Robotics Lab’s Bio-energy group. The robot runs on electricity extracted from biomass by 48 microbial fuel cells (the two concentric brick coloured rings). The robot is 90% 3D printed, and the plastic is recyclable.
I would love to see, in the near term, not only a new standard on Sustainable Robotics as a guide (and spur) for manufacturers, but the emergence of Sustainable Robotics as a thriving new sub-discipline in robotics.