I was asked to do the keynote talk at the Shift Mobility 2020 conference in Berlin on 3rd September. Via Zoom, natch. I spoke about the seductive myth of autonomous vehicles and how it could lead to bad policy decisions. Here’s a loose version of what I said.
A just-so story
In 2007, at a disused Air Force base in California, teams of researchers from around the US came together to compete in the third DARPA Grand Challenge. Two earlier competitions had taken place in the desert, with the challenge being to get a robot car to find its way along a fixed course. This time, the challenge was to navigate an ersatz town, with junctions, other competitors’ cars and actual human-driven cars to contend with. There were a few bumps and scrapes, but six teams completed the three ‘missions’, travelling 55 miles without drivers. According to one enthusiastic account, which I reviewed here, this was “the moment… when everything changed”. The world began paying attention. Self-driving cars switched from impossible to inevitable. The competition’s team members went on to populate the tech companies who would funnel billions of dollars towards a race to develop the tech.
The promise that a truly self-driving car was just around the corner was based on an observation that artificial intelligence was advancing exponentially. Following a high-profile 1997 victory of a computer over the world’s greatest chess player in, headlines were now announcing that humans were being superseded in more complicated games like Go, as well as tasks like translation, voice recognition, translation and medical diagnostics. Huge increases in computer power and available data allowed machine learning to take off. At CES 2018, the CEO of Nvidia, a chipmaker, announced that AI would soon solve self-driving.
For tech people, this was a fascinating test case for machine learning in the wild. And the social justifications seemed clear. Humans are unreliable drivers. They get drunk, distracted and old. Computers mean reliability, safety and efficiency. Consultants crunched the numbers and concluded that self-driving cars would enable an 80% reduction in the number of cars, a repurposing of the space currently devoted to parking, hundreds of thousands of lived saved and trillions of dollars of economic benefits.
YouTube is replete with videos of self-driving cars in action. This one (below) is from Tesla. It shows something remarkable: an artificial intelligence sensing and classifying objects in real time, predicting their future movements and planning a safe path through them, in bad weather, on complicated streets, with pedestrians, cyclists and other hazards. Tesla’s sensors aren’t particularly complicated. Their argument is that if human eyes are good enough then video cameras will do the job just fine. The system’s power comes from its ability to learn from data gained from sensors across its whole fleet. When one Tesla learns something about the world, they all learn it.
This story is of a plug-and-play technology, learning about and adapting to the world in all its complexity. It will change the world without needing to change the world. The story is exciting and seductive. It is also, crucially, not true.
The real history of the technology is longer and more complicated than the simple story suggests. It is a history not just of smart cars, but of smart roads. In 1956, General Motors imagined a self-driving future in its Motorama exhibit. Notwithstanding the rather fixed social roles in its utopia – men up front smoking cigars; ladies in the back – this automotive dream comes from an age before the US had given up on infrastructure. The system on offer involves “high speed safety lanes” that would allow drivers to go hands-free and serve ice cream from their glove compartment.
General Motors, an old-school carmaker, still knows that for its technology to work, the world needs to meet it halfway.
An ‘autonomous vehicles’ is far from autonomous. For the technology to work, it needs to be embedded in the social and technical world – its physical and digital infrastructures, its legal rules and its social norms and practices. These things differ from place to place, making a universal technology impossible. Nor is the technology inevitable. Its development is driven by powerful commercial interests, which may not align with the public good. We can imagine that, if the technology’s claims go unquestioned, there will be pressure to change the world to suit self-driving cars.
Take the Roomba, a robot vacuum cleaner that has quickly become a part of many homes in the affluent world. Social scientists studying how people use the Roomba have found that it is a not a simple matter of buying a robot to solve their dirt problem. It takes work to make rooms navigable and machine-readable for the Roomba. Users had to adapt their lives so that the Roomba could do its job. Some of the details are interesting:
‘One of our participants told us that she threw away her rug in the living room because her Roomba kept “getting frustrated” with the length of the shag, getting it caught in its brushes. Another participant taped down the entire tassel on the carpet every time he ran the robot. Also, we had a participant who replaced the old refrigerator with a new one that had enough space underneath for Roomba.’From Sung, Ja-Young, et al. ““My Roomba is Rambo”: intimate home appliances.” International conference on ubiquitous computing, 2007.
What does this mean for self-driving cars? The question is not when the technology will arrive, but where, for whom and in what form? In places where self-driving cars are being tested, streets are being mapped in exquisite detail and kitted out with smart traffic lights. Places are being chosen for their weather, the tidiness of their road junctions, the predictability of their pedestrians and the affluence of their potential consumers. The transition towards a self-driving future will be patchy and uneven.
Upgrading our mobility
When motor cars began arriving in US cities more than a century ago, streets were messy places in which multiple modes of transport interacted. The historian Peter Norton describes how, in the name of efficiency and safety, streets were reorganised to suit the car. The motor lobby fought hard and pedestrians lost out.
Many cities are now trying to extricate themselves from a dependence on cars that has been built into their architectures, their economies and their cultures. If we are to realise the advantages of self-driving cars without repeating the mistakes made with their predecessors, we must not sleepwalk into the technology. It is not clear what the right approach is: in the US the autonomous ideal has taken hold while in China there is a more infrastructure-first approach. But rather than starting with an imaginary technology, we should start with people’s mobility needs.