通过模拟监督评分与锚点偏移提案，提升端到端自动驾驶路径生成与选择能力。
HMAD: Advancing E2E Driving with Anchored Offset Proposals and Simulation-Supervised Multi-target Scoring
- 用贝叶斯视角的锚点查询生成多样稳定轨迹，迭代优化偏移量。
- 在CVPR 2025私有测试集上达到44.5%驾驶得分，显著优于传统方法。
- 适合关注安全感知路径评估与端到端自动驾驶系统的研究人员。
端到端自动驾驶在生成多样化且符合规则的行驶轨迹，以及从候选路径中鲁棒地选出最优路径方面仍面临挑战。为此，我们提出HMAD框架，融合基于鸟瞰图（BEV）的轨迹提案机制与多维度学习评分。该框架采用BEVFormer结构，使用可学习的锚点查询，初始化自轨迹字典，并通过受DiffusionDrive启发的迭代偏移解码进行优化，生成大量多样且稳定的候选轨迹。其核心创新在于模拟监督评分模块，可评估轨迹在无责任碰撞、可行驶区域合规性、舒适度及整体驾驶质量（即扩展版PDM分数）等方面的表现。实验证明，HMAD在CVPR 2025私有测试集上取得44.5%的驾驶得分。本工作强调了将鲁棒轨迹生成与全面安全感知的学习评分有效解耦对先进自动驾驶的重要意义。
原文摘要 · Abstract (English)
End-to-end autonomous driving faces persistent challenges in both generating diverse, rule-compliant trajectories and robustly selecting the optimal path from these options via learned, multi-faceted evaluation. To address these challenges, we introduce HMAD, a framework integrating a distinctive Bird's-Eye-View (BEV) based trajectory proposal mechanism with learned multi-criteria scoring. HMAD leverages BEVFormer and employs learnable anchored queries, initialized from a trajectory dictionary and refined via iterative offset decoding (inspired by DiffusionDrive), to produce numerous diverse and stable candidate trajectories. A key innovation, our simulation-supervised scorer module, then evaluates these proposals against critical metrics including no at-fault collisions, drivable area compliance, comfortableness, and overall driving quality (i.e., extended PDM score). Demonstrating its efficacy, HMAD achieves a 44.5% driving score on the CVPR 2025 private test set. This work highlights the benefits of effectively decoupling robust trajectory generation from comprehensive, safety-aware learned scoring for advanced autonomous driving.
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