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For both driving safety and efficiency, automated vehicles should be able topredict the behavior of surrounding traffic participants in a complex dynamicenvironment. To accomplish such a task, trajectory prediction is the key.Although many researchers have been engaged in this topic, it is stillchallenging. One of the important and inherent factors is the multi-modality ofvehicle motion. Because of the disparate driving behaviors under the samecondition, the prediction of vehicle trajectory should also be multi-modal. Atpresent, related researches have more or less shortcomings for multi-modaltrajectory prediction, such as requiring explicit modal labels or multipleforward propagation caused by sampling. In this work, we focus on overcomingthese issues by pointing out the dual-levels of multi-modal characteristics invehicle motion and proposing the dual-level stochastic multiple choice learningmethod (named as DsMCL, for short). This method does not require modal labelsand can implement a comprehensive probabilistic multi-modal trajectoryprediction by a single forward propagation. By experiments on the NGSIM andHighD datasets, our method has proven significant improvement on severaltrajectory prediction frameworks and achieves state-of-the-art performance.

DsMCL: Dual-Level Stochastic Multiple Choice Learning for Multi-Modal Trajectory Prediction