モデル構造化ニューラルネットワークによる自動運転レースカーのステアリング制御
Model-Structured Neural Networks to Control the Steering Dynamics of Autonomous Race Cars
車両の非線形ダイナミクスの事前知識をニューラルネットワーク構造に組み込んだステアリング制御器を提案し、実車レースデータで汎用NNや優勝チームの制御器より高精度・高汎化性能を示した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Mattia Piccinini, Aniello Mungiello, Georg Jank, Gastone Pietro Rosati Papini, Francesco Biral, Johannes Betz
分類: cs.RO
原文アブストラクト
Autonomous racing has gained increasing attention in recent years, as a safe environment to accelerate the development of motion planning and control methods for autonomous driving. Deep learning models, predominantly based on neural networks (NNs), have demonstrated significant potential in modeling the vehicle dynamics and in performing various tasks in autonomous driving. However, their black-box nature is critical in the context of autonomous racing, where safety and robustness demand a thorough understanding of the decision-making algorithms. To address this challenge, this paper proposes MS-NN-steer, a new Model-Structured Neural Network for vehicle steering control, integrating the prior knowledge of the nonlinear vehicle dynamics into the neural architecture. The proposed controller is validated using real-world data from the Abu Dhabi Autonomous Racing League (A2RL) competition, with full-scale autonomous race cars. In comparison with general-purpose NNs, MS-NN-steer is shown to achieve better accuracy and generalization with small training datasets, while being less sensitive to the weights' initialization. Also, MS-NN-steer outperforms the steering controller used by the A2RL winning team. Our implementation is available open-source in a GitHub repository.
関連論文
- KNODE-MPC：知識ベースのデータ駆動型予測制御フレームワークによる飛行ロボット制御制御/モデルベース学習
