DriveMamba: 以任务为中心的可扩展状态空间模型用于高效端到端自动驾驶
DriveMamba: Task-Centric Scalable State Space Model for Efficient End-to-End Autonomous Driving
AI总结：
DriveMamba通过动态任务关系建模、隐式视角对应学习和长期时间融合，提升端到端自动驾驶的效率与可扩展性。
摘要：
近年来，针对端到端自动驾驶（E2E-AD）的研究多集中于将模块化设计整合到统一框架中以实现联合优化，例如UniAD，其遵循顺序范式（即感知-预测-规划），基于可分离的Transformer解码器，并依赖密集BEV特征来编码场景表示。然而，这种手动排序设计不可避免地会导致信息丢失和累积误差，缺乏不同模块和传感器之间灵活多样的关系建模。同时，图像主干网络训练不足和注意力机制二次复杂度也阻碍了E2E-AD系统的可扩展性和效率，难以处理时空输入。为此，我们提出了DriveMamba，一种以任务为中心的可扩展范式，用于高效E2E-AD。该方法将动态任务关系建模、隐式视角对应学习和长期时间融合整合到单一阶段的统一Mamba解码器中。具体而言，提取的图像特征和预期的任务输出在提前转换为令牌级稀疏表示后，再根据其在3D空间中的实例位置进行排序。线性复杂度运算符使高效的长上下文顺序令牌建模成为可能，从而同时捕捉任务相关的相互依赖性。此外，设计了一种双向轨迹引导的
英文摘要：
Recent advances towards End-to-End Autonomous Driving (E2E-AD) have been often devoted on integrating modular designs into a unified framework for joint optimization e.g. UniAD, which follow a sequential paradigm (i.e., perception-prediction-planning) based on separable Transformer decoders and rely on dense BEV features to encode scene representations. However, such manual ordering design can inevitably cause information loss and cumulative errors, lacking flexible and diverse relation modeling among different modules and sensors. Meanwhile, insufficient training of image backbone and quadratic-complexity of attention mechanism also hinder the scalability and efficiency of E2E-AD system to handle spatiotemporal input. To this end, we propose DriveMamba, a Task-Centric Scalable paradigm for efficient E2E-AD, which integrates dynamic task relation modeling, implicit view correspondence learning and long-term temporal fusion into a single-stage Unified Mamba decoder. Specifically, both extracted image features and expected task outputs are converted into token-level sparse representations in advance, which are then sorted by their instantiated positions in 3D space. The linear-complexity operator enables efficient long-context sequential token modeling to capture task-related inter-dependencies simultaneously. Additionally, a bidirectional trajectory-guided "local-to-global" scan method is designed to preserve spatial locality from ego-perspective, thus facilitating the ego-planning. Extensive experiments conducted on nuScenes and Bench2Drive datasets demonstrate the superiority, generalizability and great efficiency of DriveMamba.
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。
