  Safe, socially compliant, and efficient navigation of low-speed autonomous
vehicles (AVs) in pedestrian-rich environments necessitates considering
pedestrians' future positions and interactions with the vehicle and others.
Despite the inevitable uncertainties associated with pedestrians' predicted
trajectories due to their unobserved states (e.g., intent), existing deep
reinforcement learning (DRL) algorithms for crowd navigation often neglect
these uncertainties when using predicted trajectories to guide policy learning.
This omission limits the usability of predictions when diverging from ground
truth. This work introduces an integrated prediction and planning approach that
incorporates the uncertainties of predicted pedestrian states in the training
of a model-free DRL algorithm. A novel reward function encourages the AV to
respect pedestrians' personal space, decrease speed during close approaches,
and minimize the collision probability with their predicted paths. Unlike
previous DRL methods, our model, designed for AV operation in crowded spaces,
is trained in a novel simulation environment that reflects realistic pedestrian
behaviour in a shared space with vehicles. Results show a 40% decrease in
collision rate and a 15% increase in minimum distance to pedestrians compared
to the state of the art model that does not account for prediction uncertainty.
Additionally, the approach outperforms model predictive control methods that
incorporate the same prediction uncertainties in terms of both performance and
computational time, while producing trajectories closer to human drivers in
similar scenarios.
