12 papers · 1 filter
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…
Hazard-Aware Traffic Scene Graph Generation
Yaoqi Huang, Julie Stephany Berrio, Mao Shan +1
Maintaining situational awareness in complex driving scenarios is challenging. It requires continuously prioritizing attention among extensive scene entities and understanding how…
TFusionOcc: T-Primitive Based Object-Centric Multi-Sensor Fusion Framework for 3D Occupancy Prediction
Zhenxing Ming, Yaoqi Huang, Julie Stephany Berrio +2
The prediction of 3D semantic occupancy enables autonomous vehicles (AVs) to perceive the fine-grained geometric and semantic scene structure for safe navigation and decision-makin…
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…
What Demands Attention in Urban Street Scenes? From Scene Understanding towards Road Safety: A Survey of Vision-driven Datasets and Studies
Yaoqi Huang, Julie Stephany Berrio, Mao Shan +1
Advances in vision-based sensors and computer vision algorithms have significantly improved the analysis and understanding of traffic scenarios. To facilitate the use of these impr…
OccCylindrical: Multi-Modal Fusion with Cylindrical Representation for 3D Semantic Occupancy Prediction
Zhenxing Ming, Julie Stephany Berrio, Mao Shan +5
The safe operation of autonomous vehicles (AVs) is highly dependent on their understanding of the surroundings. For this, the task of 3D semantic occupancy prediction divides the s…
InterLoc: LiDAR-based Intersection Localization using Road Segmentation with Automated Evaluation Method
Nguyen Hoang Khoi Tran, Julie Stephany Berrio, Mao Shan +2
Online localization of road intersections is beneficial for autonomous vehicle localization, mapping and motion planning. Intersections offer strong landmarks for correcting vehicl…
M2S-RoAD: Multi-Modal Semantic Segmentation for Road Damage Using Camera and LiDAR Data
Tzu-Yun Tseng, Hongyu Lyu, Josephine Li +3
Road damage can create safety and comfort challenges for both human drivers and autonomous vehicles (AVs). This damage is particularly prevalent in rural areas due to less frequent…
SydneyScapes: Image Segmentation for Australian Environments
Hongyu Lyu, Julie Stephany Berrio, Mao Shan +1
Autonomous Vehicles (AVs) are being partially deployed and tested across various global locations, including China, the USA, Germany, France, Japan, Korea, and the UK, but with lim…
Inverse++: Vision-Centric 3D Semantic Occupancy Prediction Assisted with 3D Object Detection
Zhenxing Ming, Julie Stephany Berrio, Mao Shan +1
3D semantic occupancy prediction aims to forecast detailed geometric and semantic information of the surrounding environment for autonomous vehicles (AVs) using onboard surround-vi…
Endangered Alert: A Field-Validated Self-Training Scheme for Detecting and Protecting Threatened Wildlife on Roads and Roadsides
Kunming Li, Mao Shan, Stephany Berrio Perez +2
Traffic accidents are a global safety concern, resulting in numerous fatalities each year. A considerable number of these deaths are caused by animal-vehicle collisions (AVCs), whi…