Lane Graph Estimation for Scene Understanding in Urban Driving

TL;DR

This paper introduces LaneGraphNet, a novel graph-based approach for estimating lane geometry and connections from bird's-eye-view images, aiding autonomous vehicle scene understanding without manual annotation.

Contribution

The paper presents a new graph estimation model that predicts lane shapes and connections from bird's-eye-view data, enabling automated HD lane annotation generation.

Findings

Promising performance on NuScenes dataset

Effective lane connection direction estimation

Potential to automate lane annotation process

Abstract

Lane-level scene annotations provide invaluable data in autonomous vehicles for trajectory planning in complex environments such as urban areas and cities. However, obtaining such data is time-consuming and expensive since lane annotations have to be annotated manually by humans and are as such hard to scale to large areas. In this work, we propose a novel approach for lane geometry estimation from bird's-eye-view images. We formulate the problem of lane shape and lane connections estimation as a graph estimation problem where lane anchor points are graph nodes and lane segments are graph edges. We train a graph estimation model on multimodal bird's-eye-view data processed from the popular NuScenes dataset and its map expansion pack. We furthermore estimate the direction of the lane connection for each lane segment with a separate model which results in a directed lane graph. We illustrate the performance of our LaneGraphNet model on the challenging NuScenes dataset and provide extensive qualitative and quantitative evaluation. Our model shows promising performance for most evaluated urban scenes and can serve as a step towards automated generation of HD lane annotations for autonomous driving.