Mimir通过不确定性建模与多速率引导，提升自动驾驶端到端轨迹生成的鲁棒性与速度。
Mimir: Hierarchical Goal-Driven Diffusion with Uncertainty Propagation for End-to-End Autonomous Driving
- 采用拉普拉斯分布估计目标点不确定性，增强决策鲁棒性
- 在Navhard/Navtest上驱动得分提升20%，推理速度加快1.6倍
- 适合追求高可靠性与实时性的自动驾驶系统开发者
端到端自动驾驶已成为自主系统领域的重要方向。近期工作通过引入高层指导信号来引导底层轨迹规划，取得了显著成果。然而，其性能常受限于高层指导信号的不准确以及复杂指导模块带来的计算开销。为此，我们提出Mimir，一种新型分层双系统框架，能够基于带不确定性估计的目标点生成鲁棒轨迹：(1) 与以往确定性建模不同，我们使用拉普拉斯分布估计目标点不确定性以提升鲁棒性；(2) 为克服指导系统推理慢的问题，引入多速率引导机制，提前预测扩展目标点。在具有挑战性的Navhard和Navtest基准上验证，Mimir在驾驶得分EPDMS上相比之前最先进方法提升20%，同时高层模块推理速度提高1.6倍，且未牺牲准确性。代码与模型将很快公开，以促进可复现性和进一步发展。代码地址：https://github.com/ZebinX/Mimir-Uncertainty-Driving
原文摘要 · Abstract (English)
End-to-end autonomous driving has emerged as a pivotal direction in the field of autonomous systems. Recent works have demonstrated impressive performance by incorporating high-level guidance signals to steer low-level trajectory planners. However, their potential is often constrained by inaccurate high-level guidance and the computational overhead of complex guidance modules. To address these limitations, we propose Mimir, a novel hierarchical dual-system framework capable of generating robust trajectories relying on goal points with uncertainty estimation: (1) Unlike previous approaches that deterministically model, we estimate goal point uncertainty with a Laplace distribution to enhance robustness; (2) To overcome the slow inference speed of the guidance system, we introduce a multi-rate guidance mechanism that predicts extended goal points in advance. Validated on challenging Navhard and Navtest benchmarks, Mimir surpasses previous state-of-the-art methods with a 20% improvement in the driving score EPDMS, while achieving 1.6 times improvement in high-level module inference speed without compromising accuracy. The code and models will be released soon to promote reproducibility and further development. The code is available at https://github.com/ZebinX/Mimir-Uncertainty-Driving
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