In this paper, we present an Efficient Planning System for automated vehicles In highLy interactive envirONments (EPSILON). EPSILON is an efficient interaction-aware planning system for automated driving, and is extensively validated in both simulation and real-world dense city traffic. It follows a hierarchical structure with an interactive behavior planning layer and an optimization-based motion planning layer. The behavior planning is formulated from a partially observable Markov decision process (POMDP), but is much more efficient than naively applying a POMDP to the decision-making problem. The key to efficiency is guided branching in both the action space and observation space, which decomposes the original problem into a limited number of closed-loop policy evaluations. Moreover, we introduce a new driver model with a safety mechanism to overcome the risk induced by the potential imperfectness of prior knowledge. For motion planning, we employ a spatio-temporal semantic corridor (SSC) to model the constraints posed by complex driving environments in a unified way. Based on the SSC, a safe and smooth trajectory is optimized, complying with the decision provided by the behavior planner. We validate our planning system in both simulations and real-world dense traffic, and the experimental results show that our EPSILON achieves human-like driving behaviors in highly interactive traffic flow smoothly and safely without being over-conservative compared to the existing planning methods.
翻译:在本文中,我们展示了一个高效的自动化车辆规划系统。EPSILON是一个高效的自动驾驶互动意识规划系统(EPSILON),它是一个高效的自动驾驶互动意识规划系统,在模拟和现实世界密集的城市交通中得到了广泛验证。它遵循一个等级结构,具有互动行为规划层和优化型运动规划层。行为规划来自一个部分可见的Markov决策程序(POMDP),但比天真地对决策问题应用POMDP(POMDP)的效率要高得多。效率的关键在于在行动空间和观测空间中进行分流引导,将最初的问题分解成数量有限的闭路政策评价。此外,我们引入了一个新的驱动器模型,带有一种安全机制,以克服先前知识的潜在不完善所带来的风险。关于运动规划,我们使用一个真空-运动设计走廊(SSC)来模拟复杂驱动环境对决策的制约。基于SSC,安全而顺畅的轨道是优化的,与行为规划计划提供的决定相一致,不遵从行为动态计划所提供的决定。我们用高密度的互联网模拟和高度机动式的系统,我们用高速度模拟了我们现有的交通规划。