10 papers
CorrelationFlow: A Training-Free Geometric Approach for LiDAR Scene Flow Estimation
Minh-Quan Dao, Yancong Lin, Julie Stephany Berrio Perez +1
LiDAR scene flow estimation has settled into a monoculture: nearly all recent methods share the same feed-forward architecture and the same family of self-supervised losses, inheri…
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…
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…
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…