SEDM: A Safety‐Enhanced Decision‐Making Framework for Autonomous Driving by Integrating Large Language Models and XGBoost

ABSTRACT Large language models (LLMs) are promising for autonomous driving decision‐making, but existing methods mostly rely on cloud‐side deployment, causing high decision latency, privacy concerns and a lack of explicit safety verification for generated actions. To address these challenges, we propose SEDM (safety‐enhanced decision‐making framework) for highway driving scenarios. SEDM comprises an environment encoding module, an edge‐side LLM‐based decision‐making module enhanced through chain‐of‐thought prompting and low‐rank adaptation (LoRA) fine‐tuning, and an XGBoost‐based safety shield module that filters unsafe actions generated by the LLM. Experiments show that SEDM achieves driving success rates of 95%, 82% and 55% under simple, normal and dense traffic conditions, respectively—substantially outperforming such as deep Q‐network and proximal policy optimization. Moreover, it yields a 17‐percentage‐point improvement in success rate over an ablated variant without the safety shield module. Furthermore, decision latency is reduced from 7.80 s (cloud‐side LLM) to 1.01 s.