ワールドエンジン：自動運転におけるポストトレーニング時代に向けて
World Engine: Towards the Era of Post-Training for Autonomous Driving
実世界のログから高忠実度のインタラクティブ環境を再構築し、安全上重要な長尾シナリオを合成的に生成して、事前学習済みの自動運転モデルを強化学習でポストトレーニングする枠組みを提案。これにより、実世界のリスクを回避しつつ安全性を向上させる。
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: Tianyu Li, Li Chen, Caojun Wang, Haochen Liu, Kashyap Chitta, Zhenjie Yang, Yuhang Lu, Naisheng Ye, Yihang Qiu, Yufei Wang, Luoxi Zou, Jiaxin Peng, Jin Pan, Zhaoyu Su, Andrei Bursuc, Shengbo Eben Li, Andreas Geiger, Peng Su, Hongyang Li
分類: cs.RO, cs.CV
原文アブストラクト
Autonomous vehicles must operate safely in the real world, where errors can have severe consequences. Although modern end-to-end driving policies excel in routine scenarios, their reliability is limited by the scarcity of safety-critical ``long-tail'' events in real driving datasets. These rare interactions define the practical safety boundary of the learned policy, yet they are difficult to collect at scale in the real world. Here we show that this fundamental limitation can be addressed by post-training pre-trained driving models on synthesized high-stakes interactions. We introduce World Engine, a generative framework that reconstructs high-fidelity interactive environments from real-world logs and systematically extrapolates them into realistic safety-critical variations. This paradigm enables reinforcement-based post-training to align policies with safety constraints, circumventing the physical risks inherent in real-world exploration. On a public benchmark built on nuPlan, World Engine substantially reduces failures in rare safety-critical scenarios and yields significantly larger gains than scaling pre-training data alone. Furthermore, when deployed on a production-scale autonomous driving system, the resulting policy reduces simulated collisions and demonstrates measurable improvements in on-road testing, showing that post-training on synthesized, safety-critical interactions offers a scalable and effective pathway to safer autonomous driving. The full codebase suite, including training, is released to the public.
