Generating Probabilistic Scenario Programs from Natural Language
arxiv(2024)
摘要
For cyber-physical systems (CPS), including robotics and autonomous vehicles,
mass deployment has been hindered by fatal errors that occur when operating in
rare events. To replicate rare events such as vehicle crashes, many companies
have created logging systems and employed crash reconstruction experts to
meticulously recreate these valuable events in simulation. However, in these
methods, "what if" questions are not easily formulated and answered. We present
ScenarioNL, an AI System for creating scenario programs from natural language.
Specifically, we generate these programs from police crash reports. Reports
normally contain uncertainty about the exact details of the incidents which we
represent through a Probabilistic Programming Language (PPL), Scenic. By using
Scenic, we can clearly and concisely represent uncertainty and variation over
CPS behaviors, properties, and interactions. We demonstrate how commonplace
prompting techniques with the best Large Language Models (LLM) are incapable of
reasoning about probabilistic scenario programs and generating code for
low-resource languages such as Scenic. Our system is comprised of several LLMs
chained together with several kinds of prompting strategies, a compiler, and a
simulator. We evaluate our system on publicly available autonomous vehicle
crash reports in California from the last five years and share insights into
how we generate code that is both semantically meaningful and syntactically
correct.
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