  For both driving safety and efficiency, automated vehicles should be able to
predict the behavior of surrounding traffic participants in a complex dynamic
environment. To accomplish such a task, trajectory prediction is the key.
Although many researchers have been engaged in this topic, it is still
challenging. One of the important and inherent factors is the multi-modality of
vehicle motion. Because of the disparate driving behaviors under the same
condition, the prediction of vehicle trajectory should also be multi-modal. At
present, related researches have more or less shortcomings for multi-modal
trajectory prediction, such as requiring explicit modal labels or multiple
forward propagation caused by sampling. In this work, we focus on overcoming
these issues by pointing out the dual-levels of multi-modal characteristics in
vehicle motion and proposing the dual-level stochastic multiple choice learning
method (named as DsMCL, for short). This method does not require modal labels
and can implement a comprehensive probabilistic multi-modal trajectory
prediction by a single forward propagation. By experiments on the NGSIM and
HighD datasets, our method has proven significant improvement on several
trajectory prediction frameworks and achieves state-of-the-art performance.
