#What’s it going to take to perfect self-driving cars?

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 maneuvers of the aircraft involved.

Highway coding

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.

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 recognizes 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.

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Published April 8, 2021 — 09:45 UTC