Lane Graph as Path: Continuity-preserving Path-wise Modeling for Online Lane Graph Construction

Abstract

Online lane graph construction is a promising but challenging task in autonomous driving. Previous methods usually model the lane graph at the pixel or piece level, and recover the lane graph by pixel-wise or piece-wise connection, which breaks down the continuity of the lane. Human drivers focus on and drive along the continuous and complete paths instead of considering lane pieces. Autonomous vehicles also require path-specific guidance from lane graph for trajectory planning. We argue that the path, which indicates the traffic flow, is the primitive of the lane graph. Motivated by this, we propose to model the lane graph in a novel path-wise manner, which well preserves the continuity of the lane and encodes traffic information for planning. We present a path-based online lane graph construction method, termed LaneGAP, which endto-end learns the path and recovers the lane graph via a Path2Graph algorithm. We qualitatively and quantitatively demonstrate the superiority of LaneGAP over conventional pixel-based and piece-based methods. Abundant visualizations show LaneGAP can cope with diverse traffic conditions. Code and models will be released for facilitating future research.

A Review of Crowdsourcing Update Methods for High-Definition Maps

“…While its main predictor resembles other single-frame network structures, it utilizes a buffer to store propagated memory features [84]. In addition to online local map construction, researchers have increasingly focused on other aspects related to HD maps, such as topological prediction [88,89]. This research involves modeling the topological structure of road networks as a set of values that can be learned through neural networks.…”