Using Language and Road Manuals to Inform Map Reconstruction for Autonomous Driving
This work addresses safe navigation for autonomous vehicles, but it is incremental as it builds on an existing model with added data sources.
The paper tackles lane-topology prediction for autonomous driving by augmenting a map-prior model with structured road metadata and lane-width priors from manuals, showing improvements in lane and traffic element detection on complex intersections.
Lane-topology prediction is a critical component of safe and reliable autonomous navigation. An accurate understanding of the road environment aids this task. We observe that this information often follows conventions encoded in natural language, through design codes that reflect the road structure and road names that capture the road functionality. We augment this information in a lightweight manner to SMERF, a map-prior-based online lane-topology prediction model, by combining structured road metadata from OSM maps and lane-width priors from Road design manuals with the road centerline encodings. We evaluate our method on two geo-diverse complex intersection scenarios. Our method shows improvement in both lane and traffic element detection and their association. We report results using four topology-aware metrics to comprehensively assess the model performance. These results demonstrate the ability of our approach to generalize and scale to diverse topologies and conditions.