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.

翻译：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，现有方法常忽视代价学习的重要性，并将其作为独立模块处理。本研究采用树结构策略规划器，提出一种针对自车条件预测与代价模型的可微分联合训练框架，从而直接提升最终规划性能。在条件预测方面，我们引入查询中心Transformer模型，实现高效的自车条件运动预测。在规划代价方面，我们提出一种具备潜在交互特征的可学习上下文感知代价函数，为可微分联合学习提供支持。我们使用真实世界的nuPlan数据集及其配套规划测试平台验证所提方法。本框架不仅与最先进规划方法性能相当，且在规划质量上超越其他基于学习方法，同时运行时效率更高。研究表明，联合训练相比两模块独立训练能显著提升性能。此外，我们发现树结构策略规划优于传统单阶段规划方法。