視覚ベースの走行軌道生成におけるドメイン汎化
Domain Generalization for Vision-based Driving Trajectory Generation
都市環境の自動運転向けに、敵対的学習と潜在変数回帰でエンコーダ・デコーダを訓練し、分布外シナリオでも汎化できる視覚ベース軌道生成手法を提案した論文。
著者: Yunkai Wang, Dongkun Zhang, Yuxiang Cui, Zexi Chen, Wei Jing, Junbo Chen, Rong Xiong, Yue Wang
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
One of the challenges in vision-based driving trajectory generation is dealing with out-of-distribution scenarios. In this paper, we propose a domain generalization method for vision-based driving trajectory generation for autonomous vehicles in urban environments, which can be seen as a solution to extend the Invariant Risk Minimization (IRM) method in complex problems. We leverage an adversarial learning approach to train a trajectory generator as the decoder. Based on the pre-trained decoder, we infer the latent variables corresponding to the trajectories, and pre-train the encoder by regressing the inferred latent variable. Finally, we fix the decoder but fine-tune the encoder with the final trajectory loss. We compare our proposed method with the state-of-the-art trajectory generation method and some recent domain generalization methods on both datasets and simulation, demonstrating that our method has better generalization ability.
