  In recent years, great efforts have been devoted to deep imitation learning
for autonomous driving control, where raw sensory inputs are directly mapped to
control actions. However, navigating through densely populated intersections
remains a challenging task due to uncertainty caused by uncertain traffic
participants. We focus on autonomous navigation at crowded intersections that
require interaction with pedestrians. A multi-task conditional imitation
learning framework is proposed to adapt both lateral and longitudinal control
tasks for safe and efficient interaction. A new benchmark called IntersectNav
is developed and human demonstrations are provided. Empirical results show that
the proposed method can achieve a success rate gain of up to 30% compared to
the state-of-the-art.
