  Advanced perception and path planning are at the core for any self-driving
vehicle. Autonomous vehicles need to understand the scene and intentions of
other road users for safe motion planning. For urban use cases it is very
important to perceive and predict the intentions of pedestrians, cyclists,
scooters, etc., classified as vulnerable road users (VRU). Intent is a
combination of pedestrian activities and long term trajectories defining their
future motion. In this paper we propose a multi-task learning model to predict
pedestrian actions, crossing intent and forecast their future path from video
sequences. We have trained the model on naturalistic driving open-source JAAD
dataset, which is rich in behavioral annotations and real world scenarios.
Experimental results show state-of-the-art performance on JAAD dataset and how
we can benefit from jointly learning and predicting actions and trajectories
using 2D human pose features and scene context.
