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【第184期】Diffusion Planner：基于Transformer的闭环自动驾驶算法
Seventy3：借助NotebookLM的能力进行论文解读，专注人工智能、大模型、机器人算法方向，让大家跟着AI一起进步。
进群添加小助手微信：seventy3_podcast
备注：小宇宙
今天的主题是：Diffusion-Based Planning for Autonomous Driving with Flexible GuidanceSummary
The provided research paper introduces Diffusion Planner, a novel method for autonomous driving that utilizes diffusion models to achieve human-like planning in complex environments. This approach jointly models motion prediction and planning without relying on traditional rule-based refinements, addressing limitations of imitation learning. By learning the gradient of a trajectory score function and using a flexible classifier guidance mechanism, Diffusion Planner can adapt its driving behavior for safety and other preferences. Evaluations on public and newly collected datasets demonstrate that this method achieves state-of-the-art closed-loop performance with strong transferability across different driving styles.
该研究提出了 Diffusion Planner，一种新型自动驾驶规划方法，利用扩散模型（diffusion models）在复杂环境中实现类人规划。该方法联合建模运动预测与规划，无需依赖传统的基于规则的优化，从而克服了模仿学习的局限性。通过学习轨迹评分函数的梯度，并引入灵活的分类器引导机制，Diffusion Planner 能够根据安全性及其他偏好自适应调整驾驶行为。实验结果表明，该方法在公开数据集和新采集数据集上的闭环性能达到最先进水平，并展现出跨不同驾驶风格的强泛化能力。
原文链接：https://arxiv.org/abs/2501.15564
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By 任雨山Seventy3：借助NotebookLM的能力进行论文解读，专注人工智能、大模型、机器人算法方向，让大家跟着AI一起进步。
进群添加小助手微信：seventy3_podcast
备注：小宇宙
今天的主题是：Diffusion-Based Planning for Autonomous Driving with Flexible GuidanceSummary
The provided research paper introduces Diffusion Planner, a novel method for autonomous driving that utilizes diffusion models to achieve human-like planning in complex environments. This approach jointly models motion prediction and planning without relying on traditional rule-based refinements, addressing limitations of imitation learning. By learning the gradient of a trajectory score function and using a flexible classifier guidance mechanism, Diffusion Planner can adapt its driving behavior for safety and other preferences. Evaluations on public and newly collected datasets demonstrate that this method achieves state-of-the-art closed-loop performance with strong transferability across different driving styles.
该研究提出了 Diffusion Planner，一种新型自动驾驶规划方法，利用扩散模型（diffusion models）在复杂环境中实现类人规划。该方法联合建模运动预测与规划，无需依赖传统的基于规则的优化，从而克服了模仿学习的局限性。通过学习轨迹评分函数的梯度，并引入灵活的分类器引导机制，Diffusion Planner 能够根据安全性及其他偏好自适应调整驾驶行为。实验结果表明，该方法在公开数据集和新采集数据集上的闭环性能达到最先进水平，并展现出跨不同驾驶风格的强泛化能力。
原文链接：https://arxiv.org/abs/2501.15564