  Trajectory prediction in urban mixed-traffic zones (a.k.a. shared spaces) is
critical for many intelligent transportation systems, such as intent detection
for autonomous driving. However, there are many challenges to predict the
trajectories of heterogeneous road agents (pedestrians, cyclists and vehicles)
at a microscopical level. For example, an agent might be able to choose
multiple plausible paths in complex interactions with other agents in varying
environments. To this end, we propose an approach named Multi-Context Encoder
Network (MCENET) that is trained by encoding both past and future scene
context, interaction context and motion information to capture the patterns and
variations of the future trajectories using a set of stochastic latent
variables. In inference time, we combine the past context and motion
information of the target agent with samplings of the latent variables to
predict multiple realistic trajectories in the future. Through experiments on
several datasets of varying scenes, our method outperforms some of the recent
state-of-the-art methods for mixed traffic trajectory prediction by a large
margin and more robust in a very challenging environment. The impact of each
context is justified via ablation studies.
