Multi-Task Conditional Imitation Learning for Autonomous Navigation at Crowded Intersections
This addresses the problem of safe and efficient interaction with pedestrians in autonomous driving, but it is incremental as it builds on existing imitation learning approaches.
The paper tackles autonomous navigation at crowded intersections by proposing a multi-task conditional imitation learning framework, achieving a success rate gain of up to 30% compared to state-of-the-art methods.
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.