  We present a system for estimating the friction of the pavement surface at
any curved road section, by arriving at a consensus estimate, based on data
from vehicles that have recently passed through that section. This estimate can
help following vehicles. To keep costs down, we depend only on standard
automotive sensors, such as the IMU, and sensors for the steering angle and
wheel speeds. Our system's workflow consists of: (i) processing of measurements
from existing vehicular sensors, to implement a virtual sensor that captures
the effect of low friction on the vehicle, (ii) transmitting short kinematic
summaries from vehicles to a road side unit (RSU), using V2X communication, and
(iii) estimating the friction coefficients, by running a machine learning
regressor at the RSU, on summaries from individual vehicles, and then combining
several such estimates.
  In designing and implementing our system over a road network, we face two key
questions: (i) should each individual road section have a local friction
coefficient regressor, or can we use a global regressor that covers all the
possible road sections? and (ii) how accurate are the resulting regressor
estimates? We test the performance of design variations of our solution, using
simulations on the commercial package Dyna4. We consider a single vehicle type
with varying levels of tyre wear, and a range of road friction coefficients. We
find that: (a) only a marginal loss of accuracy is incurred in using a global
regressor as compared to local regressors, (b) the consensus estimate at the
RSU has a worst case error of about ten percent, if the combination is based on
at least fifty recently passed vehicles, and (c) our regressors have root mean
square (RMS) errors that are less than five percent. The RMS error rate of our
system is half as that of a commercial friction estimation service.
