11 papers
CorrelationFlow: A Training-Free Geometric Approach for LiDAR Scene Flow Estimation
Minh-Quan Dao, Yancong Lin, Julie Stephany Berrio Perez +2
LiDAR scene flow is the task of estimating point-wise motion from sequential point clouds. It is essential for autonomous driving, as it provides critical information about moving…
GaussianMap: Learning Gaussian Representation for Multi-Sensor Online HD Map Construction
Hongyu Lyu, Julie Stephany Berrio Perez, Mao Shan +1
Autonomous driving systems benefit from high-definition (HD) maps that provide critical information about road infrastructure. The online construction of HD maps offers a scalable…
The Era of End-to-End Autonomy: Transitioning from Rule-Based Driving to Large Driving Models
Eduardo Nebot, Julie Stephany Berrio Perez
Autonomous driving is undergoing a shift from modular rule based pipelines toward end to end (E2E) learning systems. This paper examines this transition by tracing the evolution fr…
Blinking Beyond EAR: A Stable Eyelid Angle Metric for Driver Drowsiness Detection and Data Augmentation
Mathis Wolter, Julie Stephany Berrio Perez, Mao Shan
Detecting driver drowsiness reliably is crucial for enhancing road safety and supporting advanced driver assistance systems (ADAS). We introduce the Eyelid Angle (ELA), a novel, re…
Data Augmentation Strategies for Robust Lane Marking Detection
Flora Lian, Dinh Quang Huynh, Hector Penades +3
Robust lane detection is essential for advanced driver assistance and autonomous driving, yet models trained on public datasets such as CULane often fail to generalise across diffe…
MapRF: Weakly Supervised Online HD Map Construction via NeRF-Guided Self-Training
Hongyu Lyu, Thomas Monninger, Julie Stephany Berrio Perez +3
Autonomous driving systems benefit from high-definition (HD) maps that provide critical information about road infrastructure. The online construction of HD maps offers a scalable…
Been There, Scanned That: Nostalgia-Driven LiDAR Compression for Self-Driving Cars
Ali Khalid, Jaiaid Mobin, Sumanth Rao Appala +4
An autonomous vehicle can generate several terabytes of sensor data per day. A significant portion of this data consists of 3D point clouds produced by depth sensors such as LiDARs…
Multi-Modal Camera-Based Detection of Vulnerable Road Users
Penelope Brown, Julie Stephany Berrio Perez, Mao Shan +1
Vulnerable road users (VRUs) such as pedestrians, cyclists, and motorcyclists represent more than half of global traffic deaths, yet their detection remains challenging in poor lig…
Spotting the Unexpected (STU): A 3D LiDAR Dataset for Anomaly Segmentation in Autonomous Driving
Alexey Nekrasov, Malcolm Burdorf, Stewart Worrall +2
To operate safely, autonomous vehicles (AVs) need to detect and handle unexpected objects or anomalies on the road. While significant research exists for anomaly detection and segm…
Mixed Signals: A Diverse Point Cloud Dataset for Heterogeneous LiDAR V2X Collaboration
Katie Z Luo, Minh-Quan Dao, Zhenzhen Liu +9
Vehicle-to-everything (V2X) collaborative perception has emerged as a promising solution to address the limitations of single-vehicle perception systems. However, existing V2X data…
PointCompress3D: A Point Cloud Compression Framework for Roadside LiDARs in Intelligent Transportation Systems
Walter Zimmer, Ramandika Pranamulia, Xingcheng Zhou +2
In the context of Intelligent Transportation Systems (ITS), efficient data compression is crucial for managing large-scale point cloud data acquired by roadside LiDAR sensors. The…