PrismAD：通过语义规划器混合实现端到端自动驾驶的解耦规划
PrismAD: Decoupled Planning via Semantic Mixture-of-Planners for End-to-End Autonomous Driving
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中文总结 AI 辅助
研究针对自动驾驶规划器耦合问题，提出PrismAD框架，将场景令牌分组，用独立规划专家学习专门表示，语义感知路由器聚合预测，引入稀疏top-$K$激活，实验证明该框架性能具竞争力。
中文摘要 AI 辅助
本文提出了PrismAD，一个基于语义规划器混合的解耦端到端自动驾驶框架。现有规划器通常将异构场景令牌聚合到耦合表示空间中，迫使单个规划分支联合对代理交互、道路几何形状和驾驶意图进行建模。这种耦合可能会削弱特定因素的推理，并模糊不同规划线索的贡献。为解决此限制，PrismAD将场景令牌划分为交互、几何和意图组，并将它们分配给具有相同架构但参数分离的独立规划专家。每个专家学习专门的运动规划表示，而语义感知路由器通过用于运动预测和自我规划的单独路由权重自适应地聚合专家预测。还引入了带有噪声门控的稀疏top-$K$激活，以提高路由鲁棒性并减少不必要的专家计算。在nuScenes开环数据集和NeuroNCAP闭环基准上的大量实验表明，PrismAD具有竞争力的性能。我们的代码即将发布。
英文摘要
This letter presents PrismAD, a decoupled end-to-end autonomous driving framework based on a Semantic Mixture-of-Planners. Existing planners usually aggregate heterogeneous scene tokens into a coupled representation space, forcing a single planning branch to jointly model agent interaction, road geometry, and driving intention. Such coupling may weaken factor-specific reasoning and obscure the contribution of different planning cues. To address this limitation, PrismAD partitions scene tokens into interaction, geometry, and intent groups, and assigns them to independent planning experts with the same architecture but separate parameters. Each expert learns a specialized motion-planning representation, while a semantics-aware router adaptively aggregates expert predictions with separate routing weights for motion prediction and ego planning. Sparse top-$K$ activation with noisy gating is further introduced to improve routing robustness and reduce unnecessary expert computation. Extensive experiments on the nuScenes open-loop dataset and NeuroNCAP closed-loop benchmark demonstrate that PrismAD exhibits competitive performance. Our code will be released soon.
发表机构
- School of Vehicle and Mobility, Tsinghua University（清华大学车辆与运载学院）
- State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University（清华大学智能绿色车辆与出行国家重点实验室）
- School of Cyberspace Security, Southeast University（东南大学网络空间安全学院）
- School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）
- School of Automotive Engineering, Wuhan University of Technology（武汉理工大学汽车工程学院）
- SAIC GM Wuling Automobile Co., Ltd.（上汽通用五菱汽车股份有限公司）
机构由 AI 辅助整理，请以论文原文为准。
