Title: Using Diffusion Ensembles to Estimate Uncertainty for End-to-End Autonomous Driving Show Chinese title

Title: 利用扩散集合估计端到端自动驾驶的不确定性

Abstract: End-to-end planning systems for autonomous driving are improving rapidly, especially in closed-loop simulation environments like CARLA. Many such driving systems either do not consider uncertainty as part of the plan itself, or obtain it by using specialized representations that do not generalize. In this paper, we propose EnDfuser, an end-to-end driving system that uses a diffusion model as the trajectory planner. EnDfuser effectively leverages complex perception information like fused camera and LiDAR features, through combining attention pooling and trajectory planning into a single diffusion transformer module. Instead of committing to a single plan, EnDfuser produces a distribution of candidate trajectories (128 for our case) from a single perception frame through ensemble diffusion. By observing the full set of candidate trajectories, EnDfuser provides interpretability for uncertain, multi-modal future trajectory spaces, where there are multiple plausible options. EnDfuser achieves a competitive driving score of 70.1 on the Longest6 benchmark in CARLA with minimal concessions on inference speed. Our findings suggest that ensemble diffusion, used as a drop-in replacement for traditional point-estimate trajectory planning modules, can help improve the safety of driving decisions by modeling the uncertainty of the posterior trajectory distribution. Show Chinese abstract

Abstract: 自动驾驶端到端规划系统正在迅速改进，尤其是在像CARLA这样的闭环仿真环境中。 许多这样的驾驶系统要么没有将不确定性作为计划本身的一部分考虑，要么通过使用无法推广的专用表示来获取它。 在本文中，我们提出了EnDfuser，这是一种使用扩散模型作为轨迹规划器的端到端驾驶系统。 EnDfuser通过结合注意力池化和轨迹规划到一个单一的扩散变换模块中，有效地利用了复杂的感知信息，如融合的摄像头和LiDAR特征。 EnDfuser不是承诺单一计划，而是通过集成扩散从单个感知帧生成一组候选轨迹（我们的案例中有128个）。 通过观察这组完整的候选轨迹，EnDfuser为不确定性和多模态未来的轨迹空间提供了可解释性，其中存在多个合理的选项。 EnDfuser在CARLA的Longest6基准测试中取得了70.1的具有竞争力的驾驶分数，在推理速度上做出了最小的妥协。 我们的研究结果表明，集成扩散可以作为传统点估计轨迹规划模块的替代方案，通过建模后验轨迹分布的不确定性，有助于提高驾驶决策的安全性。