PlannerForge: LLM Agents for Scenario-Based Testing of Motion Planners in Autonomous Driving
Paper recorded by Signals 4 on 2026-09-08 in cs.CL. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-08 on arXiv · recorded by Signals 4 on 2026-09-09
Category: cs.CL · 自然语言处理 · first seen 2026-09-09
Abstract
Ensuring the safety of autonomous driving is a critical challenge. Scenario-based testing is a systematic process used to validate Autonomous Driving Systems (ADSs), but it remains a fragmented modular pipeline in which scenario generation, retrieval, modification, ADS execution, and results analysis are performed by separate tools with little interaction. Large Language Model (LLM) agents have sh
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Cite this page: PlannerForge: LLM Agents for Scenario-Based Testing of Motion Planners in Autonomous Driving: the #98 most recent of 186 cs.CL papers we have recorded (as of 2026-09-08). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/plannerforge-llm-agents-for-scenario-based-testing-of-motion-planners-in-autonom.html
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