<ul><li>Motion planning is crucial in autonomous driving, but curating datasets for training motion planners is expensive and may not capture rare critical scenarios.</li><li>Researchers propose an inexpensive method for generating diverse critical traffic scenarios to train robust motion planners.</li><li>They use scripts to represent traffic scenarios and train a Large Language Model (LLM) to generate scripts from user-specified text descriptions.</li><li>Motion planners trained with the generated synthetic data outperform those trained solely on real-world data.</li></ul>

Generating Traffic Scenarios via In-Context Learning to Learn Better Motion Planner

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