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Road user behavior prediction is one of the most critical components intrajectory planning for autonomous driving, especially in urban scenariosinvolving traffic signals. In this paper, a hierarchical framework is proposedto predict vehicle behaviors at a signalized intersection, using the trafficsignal information of the intersection. The framework is composed of twophases: a discrete intention prediction phase and a continuous trajectoryprediction phase. In the discrete intention prediction phase, a Bayesiannetwork is adopted to predict the vehicle's high-level intention, after that,maximum entropy inverse reinforcement learning is utilized to learn the humandriving model offline; during the online trajectory prediction phase, a drivercharacteristic is designed and updated to capture the different drivingpreferences between human drivers. We applied the proposed framework to one ofthe most challenging scenarios in autonomous driving: the yellow light runningscenario. Numerical experiment results are presented in the later part of thepaper which show the viability of the method. The accuracy of the Bayesiannetwork for discrete intention prediction is 91.1%, and the prediction resultsare getting more and more accurate as the yellow time elapses. The averageEuclidean distance error in continuous trajectory prediction is only 0.85 m inthe yellow light running scenario.

A hierarchical behavior prediction framework at signalized intersections