论文标题
在拥挤的十字路口的自主导航的多任务有条件模仿学习
Multi-Task Conditional Imitation Learning for Autonomous Navigation at Crowded Intersections
论文作者
论文摘要
近年来，为自主驾驶控制的深度模仿学习，付出了巨大的努力，在这些驾驶控制中，原始感觉输入直接映射到控制动作。但是，由于不确定的交通参与者引起的不确定性，通过人口稠密的交叉路口导航仍然是一项具有挑战性的任务。我们专注于需要与行人互动的拥挤的十字路口的自主导航。提出了一个多任务有条件的模仿学习框架，以适应横向和纵向控制任务，以进行安全有效的相互作用。开发了一种称为InterSectNAV的新基准，并提供了人类示范。经验结果表明，与最先进的方法相比，提出的方法可以实现高达30％的成功率。
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.
