GAPL: Grounded Action-effect Policy Learning for LLM-Based Trajectory Planning

2026-08-18

AI Summary

为解决自动驾驶轨迹规划中大语言模型的幻觉推理和控制精度问题，提出GAPL框架，通过整合基于LLM的效果评估、模拟效果校准及策略优化来提升性能。

Abstract

Trajectory planning for autonomous driving requires both high-level reasoning and precise low-level control. Large Language Models (LLMs) offer semantic-rich planning capabilities, however, their application is limited by hallucinated reasoning, poor grounding in environment dynamics, and limited numerical precision in control. We propose GAPL (Grounded Action-effect Policy Learning), a unified framework that integrates LLM-based effect estimation, simulation-based effect grounding, and policy optimization into a closed-loop system. GAPL consists of three modules: (1) an LLM-based Effect Evaluator for structured multi-dimensional action-effect estimation; (2) a Simulation-based Effect Grounder that predicts dynamics-consistent effects from simulator rollouts; and (3) an Effect-Aware Decision Maker that grounds LLM effect estimates against simulation via a distiller to guide Proximal Policy Optimization (PPO)-based policy learning. Experiments on four Highway-env scenarios demonstrate that GAPL consistently outperforms baselines, achieving average reductions of {0.76, 0.86, 2.00} in collision rate, average displacement error (ADE), and final displacement error (FDE), and an average reward gain of 1.44.

Problem

• Large Language Models
• trajectory planning
• autonomous driving
• hallucinated reasoning
• environment dynamics

Innovation

• Grounded Action-effect Policy Learning
• Large Language Models
• Simulation-based Effect Grounder
• Proximal Policy Optimization