DeepAI AI Chat
Log In Sign Up

Learning a Model for Inferring a Spatial Road Lane Network Graph using Self-Supervision

07/05/2021
by   Robin Karlsson, et al.
0

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.

READ FULL TEXT

page 1

page 5

page 6

07/23/2021

Automatic Construction of Lane-level HD Maps for Urban Scenes

High definition (HD) maps have demonstrated their essential roles in ena...
05/01/2021

Lane Graph Estimation for Scene Understanding in Urban Driving

Lane-level scene annotations provide invaluable data in autonomous vehic...
10/14/2019

Dynamic Graph Configuration with Reinforcement Learning for Connected Autonomous Vehicle Trajectories

Traditional traffic optimization solutions assume that the graph structu...
06/06/2019

Anytime Lane-Level Intersection Estimation Based on Trajectories

Estimating and understanding the current scene is an inevitable capabili...
12/22/2020

Hierarchical Recurrent Attention Networks for Structured Online Maps

In this paper, we tackle the problem of online road network extraction f...
04/27/2022

Mapping suburban bicycle lanes using street scene images and deep learning

On-road bicycle lanes improve safety for cyclists, and encourage partici...