超越航点回归：基于查询的可达自车未来代价学习用于端到端驾驶
Beyond Waypoint Regression: Query-Based Cost Learning over Reachable Ego Futures for End-to-End Driving
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中文总结 AI 辅助
提出基于查询的代价学习框架，为可达自车轨迹估计有界代价，结合应急感知聚合与MPPI混合，在nuScenes和真实日志上降低碰撞率并保持L2竞争力。
中文摘要 AI 辅助
基于航点回归的端到端规划器在开环精度上表现优异，但它们主要学习模仿专家几何轨迹，难以适应部署时的安全约束。我们提出了一种基于查询的代价学习框架，该框架为动态可达的自车轨迹查询估计有界代价，而非处理密集的BEV单元或小型回归轨迹集。紧凑的联合场景令牌能够捕获连贯的多模态智能体未来，而应急感知的代价聚合和代价引导的簇内MPPI混合将学习到的代价拓扑转化为可行的自车规划。在nuScenes数据集上，我们的方法相较于ST-P3和NMP等先前的代价估计规划器有所改进，在碰撞率上优于大多数回归基线，同时在L2误差上保持竞争力，并保留了可解释的代价接口。在真实驾驶日志上，所提出的规划器在无需微调的情况下，相比SparseDrive和Alpamayo降低了碰撞率，同时维持了多样化的候选轨迹集。
英文摘要
End-to-end planners based on waypoint regression achieve strong open-loop accuracy, but they primarily learn to mimic expert geometry and remain difficult to adapt to deployment-time safety constraints. We propose a query-based cost-learning framework that estimates bounded costs for dynamically reachable ego trajectory queries, rather than dense BEV cells or a small regressed trajectory set. Compact joint scene tokens capture coherent multimodal agent futures, while contingency-aware cost aggregation and cost-guided intra-cluster MPPI mixing convert the learned cost topology into feasible ego plans. On nuScenes, our method improves over prior cost-estimation planners such as ST-P3 and NMP, outperforms most regression baselines in collision rate, while remaining competitive in L2, and retaining an interpretable cost interface. On real-world driving logs, the proposed planner reduces collision rates compared with SparseDrive and Alpamayo without fine-tuning, while maintaining a diverse set of candidate trajectories.
发表机构
- FZI Research Center for Information Technology（FZI信息技术研究中心）
- Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
- CARIAD SE（CARIAD公司）
机构由 AI 辅助整理，请以论文原文为准。
