A simulation-to-real pipeline (HASS synthetic hard cases, scenario-aware prompts, and an image-to-ego geometry encoder) improves an MLLM's open-loop planning on nuScenes, especially in hard scenarios.
Think2drive: Efficient reinforcement learning by thinking with latent world model for autonomous driving (in carla-v2),
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.RO 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
RoboTron-Sim: Improving Real-World Driving via Simulated Hard-Case
A simulation-to-real pipeline (HASS synthetic hard cases, scenario-aware prompts, and an image-to-ego geometry encoder) improves an MLLM's open-loop planning on nuScenes, especially in hard scenarios.