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- GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories . . .
GoalFlow establishes a novel scoring mechanism that selects the most appropriate goal point from the candidate points based on scene information Furthermore, GoalFlow employs an efficient generative method, Flow Matching, to generate multimodal trajectories, and incorporates a refined scoring mechanism to select the optimal trajectory from the
- CVPR 2025｜GoalFlow：目标点驱动，解锁端到端生成式 . . .
GoalFlow通过聚类方法捕捉目标点 (goal point) 的分布特性，并设计了一套目标点评估机制，为目标点进行打分。 基于这些目标点，GoalFlow引导生成式方法Flow Matching生成高质量轨迹。
- Repo of GoalFlow: Goal-Driven Flow Matching for Multimodal . . .
We propose GoalFlow, a goal-point-based method that guides trajectory planning With a map-free evaluation and an efficient diffusion variant, Flow Matching, we reduce inference steps, achieving superior performance with just one denoising step
- CVPR2025：GoalFlow: Goal-Driven Flow Matching for . . .
GoalFlow 可分为三个部分：感知模块、目标点构建模块和轨迹规划模块。 在第一个模块中，遵循 transfuser [3]，图像和 LiDAR 被输入到两个单独的主干中，最后融合为 BEV 特征。
- HomePage of GoalFlow
To address these issues, we introduce GoalFlow, a novel method that effectively constrains the generative process to produce high-quality, multimodal trajectories specifically: 1) To resolve the trajectory divergence problem inherent in diffusion-based methods, GoalFlow constrains the generated trajectories by introducing a goal point
- GoalFlow - 朝花夕拾
GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories Generation in End-to-End Autonomous Driving 贡献 设计了一种创新的目标点确立方法，通过实验验证了其在引导生成模型进行轨迹生成方面的有效性。
- GoalFlow：面向端到端自动驾驶的多模态轨迹生成之目标 . . .
GoalFlow建立了一种新颖的评分机制，依据场景信息从候选点中选取最合适的目标点。 此外，GoalFlow采用高效的生成方法——流匹配（Flow Matching）来生成多模态轨迹，并结合精炼的评分机制从候选轨迹中选出最优解。
- CVPR25 SOTA！中科院 地平线GoalFlow：解锁端到端生成 . . .
为了解决这个问题，我们提出了一种基于goal point的生成式方法GoalFlow，通过goal point引导轨迹规划模块生成轨迹。 一方面，我们设计了一套map-free的goal point评估机制，能很好地捕捉到goal point的分布信息。
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