  Accurately predicting and inferring a driver's decision to brake is critical
for designing warning systems and avoiding collisions. In this paper we focus
on predicting a driver's intent to brake in car-following scenarios from a
perception-decision-action perspective according to his/her driving history. A
learning-based inference method, using onboard data from CAN-Bus, radar and
cameras as explanatory variables, is introduced to infer drivers' braking
decisions by combining a Gaussian mixture model (GMM) with a hidden Markov
model (HMM). The GMM is used to model stochastic relationships among variables,
while the HMM is applied to infer drivers' braking actions based on the GMM.
Real-case driving data from 49 drivers (more than three years' driving data per
driver on average) have been collected from the University of Michigan Safety
Pilot Model Deployment database. We compare the GMM-HMM method to a support
vector machine (SVM) method and an SVM-Bayesian filtering method. The
experimental results are evaluated by employing three performance metrics:
accuracy, sensitivity, specificity. The comparison results show that the
GMM-HMM obtains the best performance, with an accuracy of 90%, sensitivity of
84%, and specificity of 97%. Thus, we believe that this method has great
potential for real-world active safety systems.
