Title: Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks,
Abstract: Autonomous driving presents one of the largest problems that the robotics and
artificial intelligence communities are facing at the moment, both in terms of
difficulty and potential societal impact. Self-driving vehicles (SDVs) are
expected to prevent road accidents and save millions of lives while improving
the livelihood and life quality of many more. However, despite large interest
and a number of industry players working in the autonomous domain, there is
still more to be done in order to develop a system capable of operating at a
level comparable to best human drivers. One reason for this is high uncertainty
of traffic behavior and large number of situations that an SDV may encounter on
the roads, making it very difficult to create a fully generalizable system. To
ensure safe and efficient operations, an autonomous vehicle is required to
account for this uncertainty and to anticipate a multitude of possible
behaviors of traffic actors in its surrounding. In this work, we address this
critical problem and present a method to predict multiple possible trajectories
of actors while also estimating their probabilities. The method encodes each
actor's surrounding context into a raster image, used as input by deep
convolutional networks to automatically derive relevant features for the task.
Following extensive offline evaluation and comparison to state-of-the-art
baselines, as well as closed course tests, the method was successfully deployed
to a fleet of SDVs.