DTPP：面向自动驾驶树策略规划的可微联合条件预测与代价评估
DTPP: Differentiable Joint Conditional Prediction and Cost Evaluation for Tree Policy Planning in Autonomous Driving
AI总结：
本文提出DTPP框架，通过可微联合训练自车条件预测模型与上下文感知代价模型，结合树结构策略规划，在nuPlan数据集上以更高效率实现了优于现有学习方法的自动驾驶规划质量。
摘要：
运动预测和代价评估是自动驾驶车辆决策系统中的关键组成部分。然而，现有方法往往忽视代价学习的重要性，并将其视为相互独立的模块。在本研究中，我们采用树结构策略规划器，并提出一种可微的联合训练框架，同时训练以自车为条件的预测模型和代价模型，从而直接提升最终规划性能。在条件预测方面，我们引入了一种以查询为中心的Transformer模型，能够高效地进行以自车为条件的运动预测。在规划代价方面，我们提出了一种具有潜在交互特征的可学习上下文感知代价函数，以促进可微联合学习。我们使用真实世界的nuPlan数据集及其关联的规划测试平台验证了所提出的方法。我们的框架不仅达到了最先进规划方法的水平，而且在规划质量上优于其他基于学习的方法，同时在运行时间方面运行更加高效。我们表明，联合训练相比两个模块的分离训练能带来显著更好的性能。此外，我们发现树结构策略规划优于传统的单阶段规划方法。
英文摘要：
Motion prediction and cost evaluation are vital components in the decision-making system of autonomous vehicles. However, existing methods often ignore the importance of cost learning and treat them as separate modules. In this study, we employ a tree-structured policy planner and propose a differentiable joint training framework for both ego-conditioned prediction and cost models, resulting in a direct improvement of the final planning performance. For conditional prediction, we introduce a query-centric Transformer model that performs efficient ego-conditioned motion prediction. For planning cost, we propose a learnable context-aware cost function with latent interaction features, facilitating differentiable joint learning. We validate our proposed approach using the real-world nuPlan dataset and its associated planning test platform. Our framework not only matches state-of-the-art planning methods but outperforms other learning-based methods in planning quality, while operating more efficiently in terms of runtime. We show that joint training delivers significantly better performance than separate training of the two modules. Additionally, we find that tree-structured policy planning outperforms the conventional single-stage planning approach.
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