DriveSkel-LM: LLM-Based Driving Route Generation via Road-Name-Aware Skeletons

Recent work in transit planning shows that language models can generate structured routes from discrete network tokens. Extending this to driving is challenging because urban road networks are denser and more highly branched, and driving routes can contain long intersection sequences: decoding every intersection lengthens autoregressive generation, so an early error can cause missed road-change decisions, endpoint attachment failures, or disconnected routes. We introduce DriveSkel-LM, a road-name-aware framework for reliable driving route planning. Given a request, DriveSkel-LM predicts a compact skeleton of the origin, destination, and globally unique intersections at road-name changes, with aligned road names, preserving route-level decisions while omitting unchanged-road intersections. We adapt Qwen3-4B through intersection tokens and continued pre-training on anonymized navigation routes, road connectivity, and POI descriptions. Uniquely specified segments are reconstructed deterministically without graph search, while $A^{\ast }$ is used only for locally recoverable gaps. On temporally separated Beijing data, we evaluate DriveSkel-LM on optimal routing, road-name-constrained routing, and en-route search; on optimal routing it produces more reliable routes than full intersection-level generation, demonstrating the potential of LLM-based driving route planning.