Learning a Model for Inferring a Spatial Road Lane Network Graph using Self-Supervision
This addresses the costly and limiting reliance on preconstructed maps for autonomous driving, offering a scalable solution for real-world deployment.
The paper tackles the problem of generating lane-level road network maps for autonomous vehicles by introducing a self-supervised learning method that infers a spatially grounded graph from sensor data, proving it can generalize to new road layouts unlike prior approaches.
Interconnected road lanes are a central concept for navigating urban roads. Currently, most autonomous vehicles rely on preconstructed lane maps as designing an algorithmic model is difficult. However, the generation and maintenance of such maps is costly and hinders large-scale adoption of autonomous vehicle technology. This paper presents the first self-supervised learning method to train a model to infer a spatially grounded lane-level road network graph based on a dense segmented representation of the road scene generated from onboard sensors. A formal road lane network model is presented and proves that any structured road scene can be represented by a directed acyclic graph of at most depth three while retaining the notion of intersection regions, and that this is the most compressed representation. The formal model is implemented by a hybrid neural and search-based model, utilizing a novel barrier function loss formulation for robust learning from partial labels. Experiments are conducted for all common road intersection layouts. Results show that the model can generalize to new road layouts, unlike previous approaches, demonstrating its potential for real-world application as a practical learning-based lane-level map generator.