论文标题
MIDAS：具有自动自动导航的自适应策略的多机构互动意识的决策
MIDAS: Multi-agent Interaction-aware Decision-making with Adaptive Strategies for Urban Autonomous Navigation
论文作者
论文摘要
在拥挤，复杂的城市环境中的自主导航需要与道路上的其他特工进行互动。解决此问题的一个常见解决方案是使用预测模型来猜测其他代理的未来行动。尽管这是合理的，但它导致了过度保守的计划，因为它没有明确地模拟相互作用剂的行为的相互影响。本文构建了一种基于增强的学习方法，名为MIDAS，在该方法中，自我代理学会影响其他汽车在城市驾驶场景中的控制动作。 MIDAS使用注意力机制来处理任意数量的其他代理，并包含“驱动程序类型”参数，以学习跨不同计划目标的单一策略。我们建立了一个模拟环境，该环境可以通过大量代理和方法进行定量研究车辆之间的安全性，效率和相互作用的方法进行多种相互作用实验。 MIDAS通过广泛的实验进行了验证，我们表明（i）可以在不同的道路几何形状上起作用，（ii）导致自适应的自我政策可以轻松调整以满足诸如积极或谨慎的驾驶等性能标准，（iii）对于外部试剂的驱动政策以及（iv）的变化是强大的，并且（iv）与现有方法更有效地相互作用。
Autonomous navigation in crowded, complex urban environments requires interacting with other agents on the road. A common solution to this problem is to use a prediction model to guess the likely future actions of other agents. While this is reasonable, it leads to overly conservative plans because it does not explicitly model the mutual influence of the actions of interacting agents. This paper builds a reinforcement learning-based method named MIDAS where an ego-agent learns to affect the control actions of other cars in urban driving scenarios. MIDAS uses an attention-mechanism to handle an arbitrary number of other agents and includes a "driver-type" parameter to learn a single policy that works across different planning objectives. We build a simulation environment that enables diverse interaction experiments with a large number of agents and methods for quantitatively studying the safety, efficiency, and interaction among vehicles. MIDAS is validated using extensive experiments and we show that it (i) can work across different road geometries, (ii) results in an adaptive ego policy that can be tuned easily to satisfy performance criteria such as aggressive or cautious driving, (iii) is robust to changes in the driving policies of external agents, and (iv) is more efficient and safer than existing approaches to interaction-aware decision-making.
