TopoMLP++: Towards Strong and Scalable Lane Topology Reasoning in Autonomous Driving.

Abstract

Driving topology reasoning is an important perception task in autonomous driving, which requires detecting road centerlines (lane) and traffic elements, further reasoning their topology relationship. However, deploying this in real-world scenarios faces two significant challenges: designing high-performance pipelines and cost-effectively annotating topological data. To overcome these obstacles, we first introduce a robust "first-detect-then-reason" framework, named TopoMLP++. The core of TopoMLP++ lies in its emphasis on designing a powerful 3D lane detector that leverages global attention modeling and geometry-aware enhancements. Additionally, it includes 2D traffic detectors augmented by YOLOv8 results. After detection, MLP-based heads are employed for lane topology prediction, where we extend traditional binary classification by integrating a geometry-aware strategy, ensuring that connected points are geometrically close. To further minimize annotation efforts and facilitate data scalability, we propose an agent-based data engine that utilizes the predictions from TopoMLP++. This framework incorporates a large language model (LLM) as an agent, which first employs TopoMLP++ to generate pseudo-labels. The agent then detects potential inconsistencies in the predictions and coordinates external tools to iteratively refine the predicted lane centerlines. This iterative process ultimately boosts the performance of TopoMLP++. Experiments on the OpenLane-V2 dataset demonstrate that TopoMLP++ achieves state-of-the-art results. Its initial version is the 1st solution for 1st OpenLane Topology in IEEE CVPR Autonomous Driving Challenge. Additionally, with just 50% labeled data, TopoMLP++ augmented by our agent-based data engine achieves 96% of the performance attained by full-data training.