How slippery are Autumn leaves?

They may be a crucial ingredient in creating the stunning autumnal landscapes so beloved by photographers, but in certain situations leaves can present a significant danger to drivers.

Those in the know have long compared the effect to driving on snow.

But could the humble leaf really be as slippery as the white stuff? Ford engineers have come up with an answer – after conducting a unique experiment in order to find out.

Getting the data for the snow was the easy part. This was sourced from vehicle testing in snowy Scandinavian locations. But there was no research to show how slippery leaves were.

To fix that, the team gathered bags of leaves and used them to cover the test track at our proving ground in Belgium. They then called in the help of a friction-testing device that identifies how slippery surfaces are by rolling over them.

After testing they did indeed find that, in certain situations, the leaves were as slippery as snow.

Eddy Kasteel, development engineer.

“It was fun to conduct the experiment but there was a serious point. Most people know to slow down and drive more cautiously for snow. But far fewer of us give the same respect to roads covered in leaves – that can be just as slippery.”

Slipperiness is measured in units named after the Greek letter µ (or Mu). The more slippery the surface the lower the number. In testing, and at their most slippery, the leaves measured a µ level between 0.3 and 0.4. Typically, the same µ levels observed on snow surfaces

The same engineers helped to develop “Slippery Mode” for the new Ford Focus Active crossover that goes on sale next month.

Available in spacious five-door hatchback and wagon body styles with rugged exterior styling, the Focus Active has enhanced rough-road ability with a higher driving position.

Designed to improve traction on surfaces including ice, snow and wet leaves, “Slippery Mode” makes rapid readjustments to stability systems, acceleration and braking to help prevent the car from skidding, swerving or deviating from its intended path.

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