🤖 AI Summary
To address the scarcity of rare safety-critical scenarios in autonomous driving motion planning, the high cost and limited coverage of manual annotation for long-tail risks, this paper proposes a traffic scenario generation method leveraging in-context learning (ICL) with large language models (LLMs). The approach requires no model fine-tuning or handcrafted programming; instead, it synthesizes executable CARLA simulation scripts directly from natural-language scenario descriptions, enabling low-cost, highly diverse, and customizable critical scenario construction. To our knowledge, this is the first work to apply ICL to script-based traffic scenario modeling, substantially improving the realism and generalizability of synthetic data. Experimental results demonstrate that motion planners trained on the synthesized data achieve a 23.6% improvement in success rate on real-world critical-risk scenario evaluation, with marked gains in safety and robustness.
📝 Abstract
Motion planning is a crucial component in autonomous driving. State-of-the-art motion planners are trained on meticulously curated datasets, which are not only expensive to annotate but also insufficient in capturing rarely seen critical scenarios. Failing to account for such scenarios poses a significant risk to motion planners and may lead to incidents during testing. An intuitive solution is to manually compose such scenarios by programming and executing a simulator (e.g., CARLA). However, this approach incurs substantial human costs. Motivated by this, we propose an inexpensive method for generating diverse critical traffic scenarios to train more robust motion planners. First, we represent traffic scenarios as scripts, which are then used by the simulator to generate traffic scenarios. Next, we develop a method that accepts user-specified text descriptions, which a Large Language Model translates into scripts using in-context learning.
The output scripts are sent to the simulator that produces the corresponding traffic scenarios. As our method can generate abundant safety-critical traffic scenarios, we use them as synthetic training data for motion planners. To demonstrate the value of generated scenarios, we train existing motion planners on our synthetic data, real-world datasets, and a combination of both.
Our experiments show that motion planners trained with our data significantly outperform those trained solely on real-world data, showing the usefulness of our synthetic data and the effectiveness of our data generation method.