  Road user behavior prediction is one of the most critical components in
trajectory planning for autonomous driving, especially in urban scenarios
involving traffic signals. In this paper, a hierarchical framework is proposed
to predict vehicle behaviors at a signalized intersection, using the traffic
signal information of the intersection. The framework is composed of two
phases: a discrete intention prediction phase and a continuous trajectory
prediction phase. In the discrete intention prediction phase, a Bayesian
network is adopted to predict the vehicle's high-level intention, after that,
maximum entropy inverse reinforcement learning is utilized to learn the human
driving model offline; during the online trajectory prediction phase, a driver
characteristic is designed and updated to capture the different driving
preferences between human drivers. We applied the proposed framework to one of
the most challenging scenarios in autonomous driving: the yellow light running
scenario. Numerical experiment results are presented in the later part of the
paper which show the viability of the method. The accuracy of the Bayesian
network for discrete intention prediction is 91.1%, and the prediction results
are getting more and more accurate as the yellow time elapses. The average
Euclidean distance error in continuous trajectory prediction is only 0.85 m in
the yellow light running scenario.
