An Overview on Study of Identification of Driver Behavior Characteristics for Automotive Control

Table 2

Features of the four methods of building identification models.

Identification methods	NN	HMM	Fuzzy control theory	GMM
Algorithm features	To identify various types of driving behaviors located within relatively large amounts of stored time history data. The quality of feature parameters is crucial to the accuracy of NN.	To describe the statistical properties of stochastic processes and to identify inherent invisible states through external observation sequence.	To formulate fuzzy rules based on previous experience and then design model performance in accordance with the expectations of the designer.	A parametric approach to density estimation is able to generate arbitrarily shaped densities.
Model accuracy	Very high	Very high	High	High
Real-time performance	Fair [37]	Very good [37]	Fair [77]	The traditional GMM is poor, and the advanced GMM is good [78].
Model adaptive	—	—	—	Using the maximum a posteriori (MAP) or Bayesian adaptive algorithm to adjust parameters of GMM, personalized driver behavior model will be obtained.
Disadvantages	There is not a unified feasible method to adjust parameters (e.g., the number of NN layers) but generally subjective adjustments based on the simulation results of the models; training time is long.	HMM is not suitable for long-term forecasting system and requires artificial hypothesis for the sequence distribution of the current states.	Since its fuzzy rules are formulated based on a priori knowledge, the simulation results may deviate from the actual values.	GMM cannot obtain more efficient modeling of the time series of feature vectors than other methods do.
Applications	NN is suitable for pattern recognition that is easy to access to acquire the feature parameters, such as music recognition and speech recognition.	HMM is suitable for pattern recognition with strong time series data, such as driver’s intention recognition and speech recognition.	Fuzzy control theory is suitable for pattern recognition whose parameter range is difficult to determine.	GMM is expert in identifying short-term driving behaviors but not in long-term driving behaviors. If combined with PWARX, the model can have a good performance both in the short- and long-term driving behaviors.