ME$^3$-BEV：基于Mamba增强的深度强化学习用于端到端自动驾驶与BEV感知
ME$^3$-BEV: Mamba-Enhanced Deep Reinforcement Learning for End-to-End Autonomous Driving with BEV-Perception
AI总结：
本文提出ME$^3$-BEV框架，通过结合BEV感知与Mamba框架构建时空特征提取模型，并以此为深度强化学习的输入实现端到端自动驾驶，在CARLA模拟器中多指标优于现有模型并增强了可解释性。
摘要：
自动驾驶系统在感知复杂环境和做出实时决策方面面临重大挑战。传统模块化方法虽然具有可解释性，但存在误差传播和协调问题；而端到端学习系统虽能简化设计，却面临计算瓶颈。本文提出了一种使用深度强化学习（DRL）的自动驾驶新方法，该方法集成了鸟瞰图（BEV）感知以增强实时决策。我们引入了\ exttt{Mamba-BEV}模型，这是一种高效的时空特征提取网络，将基于BEV的感知与Mamba框架相结合进行时间特征建模。这种集成使系统能够在统一坐标系中编码车辆周围环境和道路特征，并准确建模长距离依赖关系。在此基础上，我们提出了\ exttt{ME$^3$-BEV}框架，该框架利用\ exttt{Mamba-BEV}模型作为端到端DRL的特征输入，在动态城市场景中实现了卓越的驾驶性能。我们进一步通过语义分割可视化高维特征来增强模型的可解释性，提供了对学习到的表示的深入理解。在CARLA模拟器上的大量实验表明，\ exttt{ME$^3$-BEV}在包括碰撞率和轨迹精度在内的多个指标上优于现有模型，为实时自动驾驶提供了有前景的解决方案。
英文摘要：
Autonomous driving systems face significant challenges in perceiving complex environments and making real-time decisions. Traditional modular approaches, while offering interpretability, suffer from error propagation and coordination issues, whereas end-to-end learning systems can simplify the design but face computational bottlenecks. This paper presents a novel approach to autonomous driving using deep reinforcement learning (DRL) that integrates bird's-eye view (BEV) perception for enhanced real-time decision-making. We introduce the \texttt{Mamba-BEV} model, an efficient spatio-temporal feature extraction network that combines BEV-based perception with the Mamba framework for temporal feature modeling. This integration allows the system to encode vehicle surroundings and road features in a unified coordinate system and accurately model long-range dependencies. Building on this, we propose the \texttt{ME$^3$-BEV} framework, which utilizes the \texttt{Mamba-BEV} model as a feature input for end-to-end DRL, achieving superior performance in dynamic urban driving scenarios. We further enhance the interpretability of the model by visualizing high-dimensional features through semantic segmentation, providing insight into the learned representations. Extensive experiments on the CARLA simulator demonstrate that \texttt{ME$^3$-BEV} outperforms existing models across multiple metrics, including collision rate and trajectory accuracy, offering a promising solution for real-time autonomous driving.
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。
