通过基于表征的屏蔽增强部分遮挡城市交通环境中自主智能体的安全性
Enhancing Safety for Autonomous Agents in Partly Concealed Urban Traffic Environments Through Representation-Based Shielding
AI总结：
本文提出一种基于可感知信息的强化学习状态表征方法，用于解决部分遮挡城市交叉口的安全导航问题，在安全性和能耗上显著优于基线，同时保持竞争力速度。
摘要：
在城市环境中通过无信号交叉口对自动驾驶车辆构成复杂挑战，其中视线遮挡、不可预测的行人横穿以及多样的交通参与者等问题要求高度关注碰撞预防。本文提出一种新颖的强化学习（RL）智能体状态表征，其核心围绕自主智能体可感知的信息，从而能够在先前未知的道路地图上实现安全导航。我们的方法在安全性和能耗指标上以显著优势超越多个基线模型。这些改进是在保持具有竞争力的平均行驶速度的同时实现的。我们的发现为更稳健、更可靠的自主导航策略铺平了道路，有望带来更安全、更高效的城市交通环境。
英文摘要：
Navigating unsignalized intersections in urban environments poses a complex challenge for self-driving vehicles, where issues such as view obstructions, unpredictable pedestrian crossings, and diverse traffic participants demand a great focus on crash prevention. In this paper, we propose a novel state representation for Reinforcement Learning (RL) agents centered around the information perceivable by an autonomous agent, enabling the safe navigation of previously uncharted road maps. Our approach surpasses several baseline models by a sig nificant margin in terms of safety and energy consumption metrics. These improvements are achieved while maintaining a competitive average travel speed. Our findings pave the way for more robust and reliable autonomous navigation strategies, promising safer and more efficient urban traffic environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
