规划中的感知：面向端到端自动驾驶的感知与规划耦合
Perception in Plan: Coupled Perception and Planning for End-to-End Autonomous Driving
AI总结：
本文提出VeteranAD框架，将感知融入规划过程，利用多模式锚定轨迹作为先验引导针对性感知，并结合自回归策略逐步预测轨迹，在NAVSIM和Bench2Drive数据集上取得最先进性能。
摘要：
端到端自动驾驶近年来取得了显著进展。现有方法主要遵循感知-规划范式，其中感知和规划在一个完全可微的框架内顺序执行，以实现面向规划的优化。我们通过一种规划中感知的框架设计进一步推进了这一范式，该设计将感知融入规划过程之中。这种设计有助于由随时间演变的规划目标引导的针对性感知，最终提升规划性能。基于这一见解，我们提出了VeteranAD，一个面向端到端自动驾驶的感知与规划耦合框架。通过引入多模式锚定轨迹作为规划先验，感知模块被专门设计为沿这些轨迹收集交通要素，从而实现全面且有针对性的感知。随后，规划轨迹基于感知结果和规划先验共同生成。为了使感知充分服务于规划，我们采用了一种自回归策略，逐步预测未来轨迹，同时在每一步聚焦相关区域进行针对性感知。凭借这一简单而有效的设计，VeteranAD充分释放了面向规划的端到端方法的潜力，从而带来更准确、更可靠的驾驶行为。在NAVSIM和Bench2Drive数据集上的大量实验表明，我们的VeteranAD达到了最先进的性能。
英文摘要：
End-to-end autonomous driving has achieved remarkable advancements in recent years. Existing methods primarily follow a perception-planning paradigm, where perception and planning are executed sequentially within a fully differentiable framework for planning-oriented optimization. We further advance this paradigm through a perception-in-plan framework design, which integrates perception into the planning process. This design facilitates targeted perception guided by evolving planning objectives over time, ultimately enhancing planning performance. Building on this insight, we introduce VeteranAD, a coupled perception and planning framework for end-to-end autonomous driving. By incorporating multi-mode anchored trajectories as planning priors, the perception module is specifically designed to gather traffic elements along these trajectories, enabling comprehensive and targeted perception. Planning trajectories are then generated based on both the perception results and the planning priors. To make perception fully serve planning, we adopt an autoregressive strategy that progressively predicts future trajectories while focusing on relevant regions for targeted perception at each step. With this simple yet effective design, VeteranAD fully unleashes the potential of planning-oriented end-to-end methods, leading to more accurate and reliable driving behavior. Extensive experiments on the NAVSIM and Bench2Drive datasets demonstrate that our VeteranAD achieves state-of-the-art performance.
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。
