潜在空間生成ワールドモデルによる自動運転模倣学習の共変量シフト緩和
Mitigating Covariate Shift in Imitation Learning for Autonomous Vehicles Using Latent Space Generative World Models
潜在空間の生成ワールドモデルを訓練に活用し、自動運転ポリシーの共変量シフトを抑える手法を提案。マルチビュー・クロスアテンションと学習済みシーンクエリを備えたTransformer知覚エンコーダも導入し、CARLAとDRIVE Simで閉ループ性能と外乱対応の改善を示した。
著者: Alexander Popov, Alperen Degirmenci, David Wehr, Shashank Hegde, Ryan Oldja, Alexey Kamenev, Bertrand Douillard, David Nistér, Urs Muller, Ruchi Bhargava, Stan Birchfield, Nikolai Smolyanskiy
分類: cs.RO, cs.CV, cs.LG, cs.SY, eess.SY
原文アブストラクト
We propose the use of latent space generative world models to address the covariate shift problem in autonomous driving. A world model is a neural network capable of predicting an agent's next state given past states and actions. By leveraging a world model during training, the driving policy effectively mitigates covariate shift without requiring an excessive amount of training data. During end-to-end training, our policy learns how to recover from errors by aligning with states observed in human demonstrations, so that at runtime it can recover from perturbations outside the training distribution. Additionally, we introduce a novel transformer-based perception encoder that employs multi-view cross-attention and a learned scene query. We present qualitative and quantitative results, demonstrating significant improvements upon prior state of the art in closed-loop testing in the CARLA simulator, as well as showing the ability to handle perturbations in both CARLA and NVIDIA's DRIVE Sim.
