  Recent advancements in motion planning for Autonomous Vehicles (AVs) show
great promise in using expert driver behaviors in non-stationary driving
environments. However, learning only through expert drivers needs more
generalizability to recover from domain shifts and near-failure scenarios due
to the dynamic behavior of traffic participants and weather conditions. A deep
Graph-based Prediction and Planning Policy Network (GP3Net) framework is
proposed for non-stationary environments that encodes the interactions between
traffic participants with contextual information and provides a decision for
safe maneuver for AV. A spatio-temporal graph models the interactions between
traffic participants for predicting the future trajectories of those
participants. The predicted trajectories are utilized to generate a future
occupancy map around the AV with uncertainties embedded to anticipate the
evolving non-stationary driving environments. Then the contextual information
and future occupancy maps are input to the policy network of the GP3Net
framework and trained using Proximal Policy Optimization (PPO) algorithm. The
proposed GP3Net performance is evaluated on standard CARLA benchmarking
scenarios with domain shifts of traffic patterns (urban, highway, and mixed).
The results show that the GP3Net outperforms previous state-of-the-art
imitation learning-based planning models for different towns. Further, in
unseen new weather conditions, GP3Net completes the desired route with fewer
traffic infractions. Finally, the results emphasize the advantage of including
the prediction module to enhance safety measures in non-stationary
environments.
