11 papers
A Scene Language Model for Open-Vocabulary Scene Mapping
Adam Lilja, Fabio Hübel, Siming He +6
Open-vocabulary 3D scene mapping aims to build a persistent representation of the objects in an environment. Existing systems typically rely on engineered mapping pipelines to asso…
SD-RouteFusion: Ego-Trajectory Prediction with SD-Map Route Conditioning
Sviatoslav Voloshyn, Bruno K. W. Martens, Wangxin Liu +2
This paper presents SD-RouteFusion, a deployable end-to-end ego-trajectory prediction method that fuses a front-facing camera, vehicle kinematics, and a navigation route derived fr…
Beyond Chamfer Distance: Granular Order-aware Evaluation Metric For Online Mapping
Chouaib Bencheikh Lehocine, Adam Lilja, Junsheng Fu +1
Online map estimation is a crucial component of autonomous driving systems that reduces the reliance on costly high-definition maps. State-of-the-art (SOTA) methods commonly predic…
BF-Map: Crowd-sourced Mapping with Bayesian B-spline Fusion
Yiping Xie, Yuxuan Xia, Erik Stenborg +5
Crowd-sourced mapping offers a scalable alternative to creating maps using traditional survey vehicles. Yet, existing methods either rely on prior high-definition (HD) maps or negl…
QueryOcc: Query-based Self-Supervision for 3D Semantic Occupancy
Adam Lilja, Ji Lan, Junsheng Fu +1
Learning 3D scene geometry and semantics from images is a core challenge in computer vision and a key capability for autonomous driving. Since large-scale 3D annotation is prohibit…
Driving with Context: Online Map Matching for Complex Roads Using Lane Markings and Scenario Recognition
Xin Bi, Zhichao Li, Yuxuan Xia +4
Accurate online map matching is fundamental to vehicle navigation and the activation of intelligent driving functions. Current online map matching methods are prone to errors in co…
GASP: Unifying Geometric and Semantic Self-Supervised Pre-training for Autonomous Driving
William Ljungbergh, Adam Lilja, Adam Tonderski. Arvid Laveno Ling +6
Self-supervised pre-training based on next-token prediction has enabled large language models to capture the underlying structure of text, and has led to unprecedented performance…
Exploring Semi-Supervised Learning for Online Mapping
Adam Lilja, Erik Wallin, Junsheng Fu +1
The ability to generate online maps using only onboard sensory information is crucial for enabling autonomous driving beyond well-mapped areas. Training models for this task -- pre…
Towards Accurate Ego-lane Identification with Early Time Series Classification
Yuchuan Jin, Theodor Stenhammar, David Bejmer +3
Accurate and timely determination of a vehicle's current lane within a map is a critical task in autonomous driving systems. This paper utilizes an Early Time Series Classification…
Bayesian Simultaneous Localization and Multi-Lane Tracking Using Onboard Sensors and a SD Map
Yuxuan Xia, Erik Stenborg, Junsheng Fu +1
High-definition map with accurate lane-level information is crucial for autonomous driving, but the creation of these maps is a resource-intensive process. To this end, we present…
Localization Is All You Evaluate: Data Leakage in Online Mapping Datasets and How to Fix It
Adam Lilja, Junsheng Fu, Erik Stenborg +1
The task of online mapping is to predict a local map using current sensor observations, e.g. from lidar and camera, without relying on a pre-built map. State-of-the-art methods are…