World Engine: Towards the Era of Post-Training for Autonomous Driving 文章
详细信息
- 来源站点
- ArXiv CS.CV
- 作者
- 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
- 文章类型
- NEWS
- 语言
- en
- 发布日期
- 2026-06-19
摘要
arXiv:2606.19836v1 Announce Type: cross Abstract: 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.