自動運転のエンドツーエンド計画のための微分可能最適化誘導学習への意思決定の統合
Integrating Decision-Making Into Differentiable Optimization Guided Learning for End-to-End Planning of Autonomous Vehicles
意思決定と軌道計画を微分可能な非線形最適化問題として定式化し、学習ベースの予測モジュールと組み合わせてエンドツーエンドで訓練可能な自動運転計画フレームワークを提案した。Waymo Open Motionデータセットで評価し、安全性・効率・乗り心地が向上することを示した。
著者: Wenru Liu, Yongkang Song, Chengzhen Meng, Zhiyu Huang, Haochen Liu, Chen Lv, Jun Ma
分類: cs.RO
原文アブストラクト
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.
