  The authors present a cyber-physical systems study on the estimation of
driver behavior in autonomous vehicles and vehicle safety systems. Extending
upon previous work, the approach described is suitable for the long term
estimation and tracking of autonomous vehicle behavior. The proposed system
makes use of a previously defined Hybrid State System and Hidden Markov Model
(HSS+HMM) system which has provided good results for driver behavior
estimation. The HSS+HMM system utilizes the hybrid characteristics of
decision-behavior coupling of many systems such as the driver and the vehicle,
uses Kalman Filter estimates of observable parameters to track the
instantaneous continuous state, and estimates the most likely driver state. The
HSS+HMM system is encompassed in a HSS structure and inter-system connectivity
is determined by using Signal Processing and Pattern Recognition techniques.
The proposed method is suitable for scenarios that involve unknown decisions of
other individuals, such as lane changes or intersection precedence/access. The
long term driver behavior estimation system involves an extended HSS+HMM
structure that is capable of including external information in the estimation
process. Through the grafting and pruning of metastates, the HSS+HMM system can
be dynamically updated to best represent driver choices given external
information. Three application examples are also provided to elucidate the
theoretical system.
