  Driving styles have a great influence on vehicle fuel economy, active safety,
and drivability. To recognize driving styles of path-tracking behaviors for
different divers, a statistical pattern-recognition method is developed to deal
with the uncertainty of driving styles or characteristics based on probability
density estimation. First, to describe driver path-tracking styles, vehicle
speed and throttle opening are selected as the discriminative parameters, and a
conditional kernel density function of vehicle speed and throttle opening is
built, respectively, to describe the uncertainty and probability of two
representative driving styles, e.g., aggressive and normal. Meanwhile, a
posterior probability of each element in feature vector is obtained using full
Bayesian theory. Second, a Euclidean distance method is involved to decide to
which class the driver should be subject instead of calculating the complex
covariance between every two elements of feature vectors. By comparing the
Euclidean distance between every elements in feature vector, driving styles are
classified into seven levels ranging from low normal to high aggressive.
Subsequently, to show benefits of the proposed pattern-recognition method, a
cross-validated method is used, compared with a fuzzy logic-based
pattern-recognition method. The experiment results show that the proposed
statistical pattern-recognition method for driving styles based on kernel
density estimation is more efficient and stable than the fuzzy logic-based
method.
