面向端到端多模态规划的广义轨迹评分
Generalized Trajectory Scoring for End-to-end Multimodal Planning
AI总结：
本文提出统一轨迹评分框架 GTRS，通过扩散生成、超密集词表泛化训练与传感器增强，兼顾粗/细粒度评估并显著提升端到端多模态规划的鲁棒性与域外泛化。
摘要：
端到端多模态规划是自动驾驶中一种很有前景的范式，能够基于多样化轨迹候选进行决策。其关键组成部分是一个鲁棒的轨迹评分器，能够从这些候选中选择最优轨迹。近期的轨迹评分器主要关注对大规模静态轨迹集合或小规模动态生成轨迹集合进行评分，但这两种方法在泛化方面都面临显著局限。静态词表提供了有效的粗粒度离散化，却难以进行细粒度适配；动态提议具备精细精度，却无法覆盖更广泛的轨迹分布。为克服这些挑战，我们提出 GTRS（Generalized Trajectory Scoring，广义轨迹评分），这是一个用于端到端多模态规划的统一框架，结合了粗粒度与细粒度轨迹评估。GTRS 包含三项互补创新：（1）基于扩散的轨迹生成器，可产生多样化的细粒度提议；（2）词表泛化技术，通过 dropout 正则化在超密集轨迹集合上训练评分器，使其能够在更小的子集上进行鲁棒推理；（3）传感器增强策略，在引入用于关键轨迹判别的细化训练的同时，提升域外泛化能力。作为 Navsim v2 Challenge 的获胜方案，GTRS 即使在次优传感器输入下也展现出卓越性能，接近依赖真值感知的特权方法。代码将在 https://github.com/NVlabs/GTRS 提供。
英文摘要：
End-to-end multi-modal planning is a promising paradigm in autonomous driving, enabling decision-making with diverse trajectory candidates. A key component is a robust trajectory scorer capable of selecting the optimal trajectory from these candidates. While recent trajectory scorers focus on scoring either large sets of static trajectories or small sets of dynamically generated ones, both approaches face significant limitations in generalization. Static vocabularies provide effective coarse discretization but struggle to make fine-grained adaptation, while dynamic proposals offer detailed precision but fail to capture broader trajectory distributions. To overcome these challenges, we propose GTRS (Generalized Trajectory Scoring), a unified framework for end-to-end multi-modal planning that combines coarse and fine-grained trajectory evaluation. GTRS consists of three complementary innovations: (1) a diffusion-based trajectory generator that produces diverse fine-grained proposals; (2) a vocabulary generalization technique that trains a scorer on super-dense trajectory sets with dropout regularization, enabling its robust inference on smaller subsets; and (3) a sensor augmentation strategy that enhances out-of-domain generalization while incorporating refinement training for critical trajectory discrimination. As the winning solution of the Navsim v2 Challenge, GTRS demonstrates superior performance even with sub-optimal sensor inputs, approaching privileged methods that rely on ground-truth perception. Code will be available at https://github.com/NVlabs/GTRS.
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。
