  A multi-modal framework to generate user intention distributions when
operating a mobile vehicle is proposed in this work. The model learns from past
observed trajectories and leverages traversability information derived from the
visual surroundings to produce a set of future trajectories, suitable to be
directly embedded into a perception-action shared control strategy on a mobile
agent, or as a safety layer to supervise the prudent operation of the vehicle.
We base our solution on a conditional Generative Adversarial Network with
Long-Short Term Memory cells to capture trajectory distributions conditioned on
past trajectories, further fused with traversability probabilities derived from
visual segmentation with a Convolutional Neural Network. The proposed
data-driven framework results in a significant reduction in error of the
predicted trajectories (versus the ground truth) from comparable strategies in
the literature (e.g. Social-GAN) that fail to account for information other
than the agent's past history. Experiments were conducted on a dataset
collected with a custom wheelchair model built onto the open-source urban
driving simulator CARLA, proving also that the proposed framework can be used
with a small, un-annotated dataset.
