Statistical Pattern Recognition for Driving Styles Based on Bayesian Probability and Kernel Density Estimation
This work addresses driving style recognition for improving vehicle fuel economy, safety, and drivability, but it is incremental as it builds on existing statistical methods with a new classification approach.
The paper tackled the problem of recognizing driving styles (e.g., aggressive vs. normal) using vehicle speed and throttle opening data, and found that their statistical pattern-recognition method based on kernel density estimation and Bayesian probability was more efficient and stable than a fuzzy logic-based method in experiments.
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.