【2/5】🎉Highlights
- Data-driven long-tail discovery: Failure-prone scenarios are automatically identified from real-world driving logs by the pre-trained agent itself — no manual design, no synthetic perturbations.
Photorealistic interactive simulation via 3DGS: Each discovered scenario is reconstructed into a fully controllable, real-time-renderable simulation environment with independent dynamic agent manipulation.
- Behavior-driven scenario generation: Leverages Behavior World Model (BWM) to generalize and synthesize diverse traffic variations from existing long-tail scenarios, expanding sparse safety-critical events into a dense, learnable distribution.
- RL-based post-training on synthesized safety-critical rollouts substantially outperforms scaling pre-training data alone — competitive with a ~10× increase in pre-training data.
- Production-scale validation: Deployed on a mass-produced ADAS platform trained on 80,000 hours of real-world driving logs, reducing simulated collision rate by up to 45.5% and achieving zero disengagements in a 200 km on-road test.
