解耦场景感知与自车状态：一种提升端到端自动驾驶泛化性的多上下文融合方法
Decoupling Scene Perception and Ego Status: A Multi-Context Fusion Approach for Enhanced Generalization in End-to-End Autonomous Driving
AI总结：
针对端到端自动驾驶过度依赖自车状态、泛化性不足的问题，提出AdaptiveAD架构，通过双分支解耦场景感知与自车状态并自适应融合，辅以注意力机制与辅助任务，在nuScenes上取得SOTA开环规划性能，泛化能力优异。
摘要：
面向规划的模块化自动驾驶设计已显著推动端到端系统发展，但现有架构仍受限于对自车状态的过度依赖，阻碍了泛化能力与鲁棒场景理解性能提升。我们发现其根源在于这类架构的固有设计使得自车状态易被作为捷径利用：具体而言，自车状态在上游BEV编码器中的过早融合，导致这一强先验的信息流主导了下游规划模块。为解决该问题，我们提出AdaptiveAD，这是一种基于多上下文融合策略的架构级解决方案，核心为显式解耦场景感知与自车状态的双分支结构：其中一个分支基于多任务学习执行场景驱动推理，且在BEV编码器中刻意剔除自车状态输入；另一个分支仅基于规划任务执行自车驱动推理。随后场景感知融合模块自适应整合两个分支的互补决策，生成最终规划轨迹。为确保该解耦设计不会损害多任务学习效果，我们引入用于自车-BEV交互的路径注意力机制，并增设两项针对性辅助任务：BEV单向蒸馏与自回归在线建图。在nuScenes数据集上的大量评估表明，AdaptiveAD取得了当前最优的开环规划性能，关键是其显著缓解了对自车状态的过度依赖，在多样场景下展现出出色的泛化能力。
英文摘要：
Modular design of planning-oriented autonomous driving has markedly advanced end-to-end systems. However, existing architectures remain constrained by an over-reliance on ego status, hindering generalization and robust scene understanding. We identify the root cause as an inherent design within these architectures that allows ego status to be easily leveraged as a shortcut. Specifically, the premature fusion of ego status in the upstream BEV encoder allows an information flow from this strong prior to dominate the downstream planning module. To address this challenge, we propose AdaptiveAD, an architectural-level solution based on a multi-context fusion strategy. Its core is a dual-branch structure that explicitly decouples scene perception and ego status. One branch performs scene-driven reasoning based on multi-task learning, but with ego status deliberately omitted from the BEV encoder, while the other conducts ego-driven reasoning based solely on the planning task. A scene-aware fusion module then adaptively integrates the complementary decisions from the two branches to form the final planning trajectory. To ensure this decoupling does not compromise multi-task learning, we introduce a path attention mechanism for ego-BEV interaction and add two targeted auxiliary tasks: BEV unidirectional distillation and autoregressive online mapping. Extensive evaluations on the nuScenes dataset demonstrate that AdaptiveAD achieves state-of-the-art open-loop planning performance. Crucially, it significantly mitigates the over-reliance on ego status and exhibits impressive generalization capabilities across diverse scenarios.
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。
