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To navigate safely in urban environments, an autonomous vehicle (ego vehicle)must understand and anticipate its surroundings, in particular the behavior andintents of other road users (neighbors). Most of the times, multiple decisionchoices are acceptable for all road users (e.g., turn right or left, ordifferent ways of avoiding an obstacle), leading to a highly uncertain andmulti-modal decision space. We focus here on predicting multiple feasiblefuture trajectories for both ego vehicle and neighbors through a probabilisticframework. We rely on a conditional imitation learning algorithm, conditionedby a navigation command for the ego vehicle (e.g., "turn right"). Our modelprocesses 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 itsneighbors. Our approach is computationally efficient and relies only onon-board sensors. We evaluate our method offline on the publicly availabledataset nuScenes, achieving state-of-the-art performance, investigate theimpact of our architecture choices on online simulated experiments and showpreliminary insights for real vehicle control

PLOP: Probabilistic poLynomial Objects trajectory Planning for autonomous driving