将决策制定集成到可微优化引导学习中以实现自动驾驶车辆的端到端规划
Integrating Decision-Making Into Differentiable Optimization Guided Learning for End-to-End Planning of Autonomous Vehicles
AI总结：
本文提出将决策与轨迹规划建模为可微非线性优化并融合神经网络预测器，构建端到端自动驾驶规划框架，在 Waymo Open Motion 上验证其安全、效率与舒适性优势。
摘要：
我们在一个聚焦于运动预测、决策制定和轨迹规划的端到端规划框架内研究决策能力。具体而言，我们将决策制定和轨迹规划表述为一个可微非线性优化问题，从而确保其与基于学习的模块兼容，以建立端到端可训练架构。该优化引入了与安全性、行驶效率和乘坐舒适性相关的显式目标，在我们提出的流程中引导学习过程。由决策任务产生的内在约束被集成到优化表述中，并在整个学习过程中得到保留。通过将可微优化器与神经网络预测器相结合，所提出的框架可端到端训练，使各种驾驶任务与优化目标所定义的最终性能目标保持一致。所提出的框架使用 Waymo Open Motion 数据集进行训练和验证。开环测试表明，尽管使用我们方法得到的规划结果并不总是与专家轨迹相似，但它们在安全性、行驶效率和乘坐舒适性方面均有提升，并持续优于基线方法。闭环测试进一步证明了优化决策和提升驾驶性能的有效性。消融研究表明，基于学习的预测模块提供的初始化对于优化器的收敛以及整体驾驶性能都至关重要。
英文摘要：
We address the decision-making capability within an end-to-end planning framework that focuses on motion prediction, decision-making, and trajectory planning. Specifically, we formulate decision-making and trajectory planning as a differentiable nonlinear optimization problem, which ensures compatibility with learning-based modules to establish an end-to-end trainable architecture. This optimization introduces explicit objectives related to safety, traveling efficiency, and riding comfort, guiding the learning process in our proposed pipeline. Intrinsic constraints resulting from the decision-making task are integrated into the optimization formulation and preserved throughout the learning process. By integrating the differentiable optimizer with a neural network predictor, the proposed framework is end-to-end trainable, aligning various driving tasks with ultimate performance goals defined by the optimization objectives. The proposed framework is trained and validated using the Waymo Open Motion dataset. The open-loop testing reveals that while the planning outcomes using our method do not always resemble the expert trajectory, they consistently outperform baseline approaches with improved safety, traveling efficiency, and riding comfort. The closed-loop testing further demonstrates the effectiveness of optimizing decisions and improving driving performance. Ablation studies demonstrate that the initialization provided by the learning-based prediction module is essential for the convergence of the optimizer as well as the overall driving performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
