  To navigate safely in urban environments, an autonomous vehicle (ego vehicle)
must understand and anticipate its surroundings, in particular the behavior and
intents of other road users (neighbors). Most of the times, multiple decision
choices are acceptable for all road users (e.g., turn right or left, or
different ways of avoiding an obstacle), leading to a highly uncertain and
multi-modal decision space. We focus here on predicting multiple feasible
future trajectories for both ego vehicle and neighbors through a probabilistic
framework. We rely on a conditional imitation learning algorithm, conditioned
by a navigation command for the ego vehicle (e.g., "turn right"). Our model
processes ego vehicle front-facing camera images and bird-eye view grid,
computed from Lidar point clouds, with detections of past and present objects,
in order to generate multiple trajectories for both ego vehicle and its
neighbors. Our approach is computationally efficient and relies only on
on-board sensors. We evaluate our method offline on the publicly available
dataset nuScenes, achieving state-of-the-art performance, investigate the
impact of our architecture choices on online simulated experiments and show
preliminary insights for real vehicle control
