# Predictive traction control

Yesterday I wrote about predictive suspension, to look ahead for bumps on the road and ready the suspension to compensate. There should be more we can learn by looking at the surface of the road ahead, or perhaps touching it, or perhaps getting telemetry from other cars.

It would be worthwhile to be able to estimate just how much traction there is on the road surfaces the tires will shortly be moving over. Traction can be estimated from the roughness of dry surfaces, but is most interesting for wet and frozen surfaces. It seems likely that remote sensing can tell the temperature of a surface, and whether it is wet or not. Wet ice is more slippery than colder ice. It would be interesting to research techniques for estimating traction well in front of the car. This could of course be used to slow the car down to the point that it can stop more easily, and to increase gaps between cars. However, it might do much more.

A truly accurate traction measurement could come by actually moving wheels at slightly different speeds. Perhaps just speeding up wheels at two opposite corners (very slightly) or slowing them down could measure traction. Or perhaps it would make more sense to have a small probe wheel at the front of the car that is always measuring traction in icy conditions. Of course, anything learned by the front wheels about traction could be used by the rear wheels.

For example, even today an anti-lock brake system could, knowing the speed of the vehicle, notice when the front wheels lock up and predict when the rear wheels will be over that same stretch of road. Likewise if they grip, it could be known as a good place to apply more braking force when the rear wheels go over.

In addition, this is something cars could share information about. Each vehicle that goes over a stretch of road could learn about the surface, and transmit that for cars yet to come, with timestamps of course. One car might make a very accurate record of the road surface that other cars passing by soon could use. If for nothing else, this would allow cars to know what a workable speed and inter-car gap is. This needs positioning more accurate that GPS, but that could easily be attained with mile marker signs on the side of the road that an optical scanner can read, combined with accurate detection of the dotted lines marking the lanes. GPS can tell you what lane you're in if you can't figure it out. Lane markers could themselves contain barcodes if desired -- highly redundant barcodes that would tolerate lots of missing pieces of course.

This technology could be applied long before the cars drive themselves. It's a useful technology for a human driven car where the human driver gets advice and corrections from an in-car system. "Slow down, there's a patch of ice ahead" could save lives. I've predicted that the roadmap to the self-driving car involves many incremental improvements which can be sold in luxury human-driven cars to make them safer and eventually accident proof. This could be a step.

### Would you rely on that?

The big question is always: if driving is made safer, will people rely on that and drive more riskily?

Will your system detect loose sand or gravel on the road ahead? If it relies on communication a lot, the first vehicle coming along after such a condition change is bound to be in for a surprise.

If you use a communicating system, the traction data stream is going to offer very fine-grained data about the position of every vehicle on the road. If you want to ambush a certain vehicle, you can do that automatically if you prepare a traction change (i.e. wet the road surface) where your ambush is.

You can also slow down cars by transmitting a fake report about ice on the road.

I am surprised that you care about ebay buyers revealing their bids in advance (in the sniping post) but don't mind revealing to everyone where you and your car are.

### Revealing

You don't have to reveal where you are, but yes, you would like a reputation on the data you get -- but not necessarily one that goes back to the real world.