  We propose a safe DRL approach for autonomous vehicle (AV) navigation through
crowds of pedestrians while making a left turn at an unsignalized intersection.
Our method uses two long-short term memory (LSTM) models that are trained to
generate the perceived state of the environment and the future trajectories of
pedestrians given noisy observations of their movement. A future collision
prediction algorithm based on the future trajectories of the ego vehicle and
pedestrians is used to mask unsafe actions if the system predicts a collision.
The performance of our approach is evaluated in two experiments using the
high-fidelity CARLA simulation environment. The first experiment tests the
performance of our method at intersections that are similar to the training
intersection and the second experiment tests our method at intersections with a
different topology. For both experiments, our methods do not result in a
collision with a pedestrian while still navigating the intersection at a
reasonable speed.
