Title: Short-term Motion Prediction of Traffic Actors for Autonomous Driving using Deep Convolutional Networks,
Abstract: Despite its ubiquity in our daily lives, AI is only just starting to make
advances in what may arguably have the largest societal impact thus far, the
nascent field of autonomous driving. In this work we discuss this important
topic and address one of crucial aspects of the emerging area, the problem of
predicting future state of autonomous vehicle's surrounding necessary for safe
and efficient operations. We introduce a deep learning-based approach that
takes into account current world state and produces rasterized representations
of each actor's vicinity. The raster images are then used by deep convolutional
models to infer future movement of actors while accounting for inherent
uncertainty of the prediction task. Extensive experiments on real-world data
strongly suggest benefits of the proposed approach. Moreover, following
successful tests the system was deployed to a fleet of autonomous vehicles.