  The comprehension of environmental traffic situation largely ensures the
driving safety of autonomous vehicles. Recently, the mission has been
investigated by plenty of researches, while it is hard to be well addressed due
to the limitation of collective influence in complex scenarios. These
approaches model the interactions through the spatial relations between the
target obstacle and its neighbors. However, they oversimplify the challenge
since the training stage of the interactions lacks effective supervision. As a
result, these models are far from promising. More intuitively, we transform the
problem into calculating the interaction-aware drivable spaces and propose the
CNN-based VisionNet for trajectory prediction. The VisionNet accepts a sequence
of motion states, i.e., location, velocity, and acceleration, to estimate the
future drivable spaces. The reified interactions significantly increase the
interpretation ability of the VisionNet and refine the prediction. To further
advance the performance, we propose an interactive loss to guide the generation
of the drivable spaces. Experiments on multiple public datasets demonstrate the
effectiveness of the proposed VisionNet.
