World Engine generates realistic safety-critical driving variations from logs for reinforcement post-training, reducing benchmark failures more than data scaling and showing collision reductions plus on-road gains in a production system.
Prediction of driver alertness levels on mountain roads using machine learning models: A naturalistic driving study in china
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
World Engine: Towards the Era of Post-Training for Autonomous Driving
World Engine generates realistic safety-critical driving variations from logs for reinforcement post-training, reducing benchmark failures more than data scaling and showing collision reductions plus on-road gains in a production system.