The road is calling and I must drive safely: Perception and self-explaining roads

How might a road tell you what to expect, and would this keep everyone who uses it safer? Since it’s probably a bad idea to have the road start to talk to drivers, cyclists and pedestrians, how should we design it so that everyone who uses it can do so safely? This question has interested Jan Theeuwes (pictured below) and his collaborators since the 1990s, but in his new paper in Cognitive Research: Principles and Implications, Theeuwes looks at the idea of self-explaining roads from a different angle – how the principles articulated 25 years ago reflect how we perceive the world.

Theeuwes Author

Letting the road speak to users

Since the road cannot, in fact, speak to us – and it would be very disconcerting if it did – how might a road explain itself? Theeuwes’ idea of self-explaining roads takes a very human-centered approach to this problem; rather than trying to design a perfect road and make the humans who use it adapt to it, self-explaining roads are designed around how we perceive our environment. Broadly, a self-explaining road is one that guides users – drivers, cyclists, motorcycle riders, pedestrians, everyone – to use it in a way that is safe with a minimum of overt instruction. If you are driving down the road, you do not have time to read a list of instructions when you pull off the highway and on to a local road, you need to know how to behave based on how you understand the environment you are in.

Theeuwes 2021 self Explain Roads
Examples of Self-Explaining Roads. The road on the left is clearly a grade-separated highway, where vehicles will move rapidly and drivers should not expect pedestrians or cyclists, whereas the road on the right (a woonerf in the Netherlands, or a shared space) is meant to be safely used by drivers, cyclists and pedestrians.

Theeuwes argues that the fundamental principles that makes a road self-explaining to its users are that it needs to be easily recognizable, easily distinguishable and easily interpretable. If we are to recognize a road for what it is, and, crucially, identify what kind of road we are looking at, this needs to be as easy as possible. While we might learn something interesting about how we perceive the world by intentionally making a particular road hard to recognize for what it is – think adding an out of place object, like a clown riding a unicycle on a highway – it’s a very bad idea from a safety perspective, because it violates recognizability. Roads that help their users also need to be easily distinguished from each other, because the behavior that is safe on a highway is very much not safe on a side street through populated areas (even if the occasional joyrider thinks otherwise!). Highways need to look like highways and connector roads need to look like connector roads so everyone on them knows what to expect. Finally, well-designed roads need to be easily interpreted, because their users have to understand their environment and how they need to behave very quickly, lest they become a danger to everyone else.

How do we tell a road to speak to us?

While these principles are simple and effective, Theeuwes goes one step further in this paper and asks how fundamental processes in visual cognition operate when it comes to our ability to intuit how to behave on particular roads. He points out that, in essence, we are all experts at categorizing our environments and we become experts at this by learning the statistical regularities of our environments simply as a result of moving through the world. We do this all the time, whether we are walking down the sidewalk, barreling down the highway, or just walking down the hall to get away from our computer.

Even in the present times, when many of us have barely been leaving the house due to the ongoing pandemic, we are still learning the statistical structure of our environments – you probably have a better mental model of the interior of your home than you did before March 2020, just because you have spent so much time there. Given this, if you walked away from your desk and your chair was replaced by a big plastic bucket, you would probably notice quite easily, since you expect a chair at your desk – but if that same bucket was placed somewhere you were unlikely to look (maybe on top of the door?), you would be much less likely to notice it. The same principle of expectation and expectation violation underlies much of the idea of self-explaining roads, because what we expect, and the expertise we have in understanding the world, is essential to our ability to move through the world with a minimum of mental friction.

Do self-explaining roads make us safer?

A key question with an idea like self-explaining roads is whether the principles actually translate to the world – if roads are designed to keep all their users safe and to encourage them to behave in ways that are better for everyone, is it worthwhile? In the 25 years since Theeuwes and Godthelp first proposed them, the answer is very much yes. These principles have been widely adopted in the European Union as well as in Australia and in New Zealand, and changing roads infrastructure to reflect these ideas has been shown to reduce speed and encourages everyone to use the roads, rather than making them the exclusive purview of some, rather than all, of us. Crucially, the central idea here is that we should understand how we perceive and think about the world, and build with those affordances in mind, rather than the other way around.

Featured Psychonomic Society article

Theeuwes, J. (2021). Self-explaining roads: What does visual cognition tell us about designing safer roads? Cognitive Research: Principles and Implications, 6(1), 1-15. https://doi.org/10.1186/s41235-021-00281-6

Author

  • Wolfe Ben Thumbnail

    Benjamin Wolfe is an Assistant Professor in the Department of Psychology at the University of Toronto, Mississauga. His research sits at the intersection of applied and basic vision science, including questions of visual perception in driving, improving readability and extending our understanding of visual perception in real-world settings.

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