  A profound understanding of inter-agent relationships and motion behaviors is
important to achieve high-quality planning when navigating in complex
scenarios, especially at urban traffic intersections. We present a trajectory
prediction approach with respect to traffic lights, D2-TPred, which uses a
spatial dynamic interaction graph (SDG) and a behavior dependency graph (BDG)
to handle the problem of discontinuous dependency in the spatial-temporal
space. Specifically, the SDG is used to capture spatial interactions by
reconstructing sub-graphs for different agents with dynamic and changeable
characteristics during each frame. The BDG is used to infer motion tendency by
modeling the implicit dependency of the current state on priors behaviors,
especially the discontinuous motions corresponding to acceleration,
deceleration, or turning direction. Moreover, we present a new dataset for
vehicle trajectory prediction under traffic lights called VTP-TL. Our
experimental results show that our model achieves more than {20.45% and 20.78%
}improvement in terms of ADE and FDE, respectively, on VTP-TL as compared to
other trajectory prediction algorithms. The dataset and code are available at:
https://github.com/VTP-TL/D2-TPred.
