104 citations · 146 across the 27 of their papers we have counts for
31 papers
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…
InterKey: Cross-modal Intersection Keypoints for Global Localization on OpenStreetMap
Nguyen Hoang Khoi Tran, Julie Stephany Berrio, Mao Shan +1
Reliable global localization is critical for autonomous vehicles, especially in environments where GNSS is degraded or unavailable, such as urban canyons and tunnels. Although high…
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…
Panoptic-CUDAL: Rural Australia Point Cloud Dataset in Rainy Conditions
Tzu-Yun Tseng, Alexey Nekrasov, Malcolm Burdorf +5
Existing autonomous driving datasets are predominantly oriented towards well-structured urban settings and favourable weather conditions, leaving the complexities of rural environm…
Performance Assessment of Lidar Odometry Frameworks: A Case Study at the Australian Botanic Garden Mount Annan
Mohamed Mourad Ouazghire, Julie Stephany Berrio, Mao Shan +1
Autonomous vehicles are being tested in diverse environments worldwide. However, a notable gap exists in evaluating datasets representing natural, unstructured environments such as…
Label-Efficient 3D Object Detection For Road-Side Units
Minh-Quan Dao, Holger Caesar, Julie Stephany Berrio +4
Occlusion presents a significant challenge for safety-critical applications such as autonomous driving. Collaborative perception has recently attracted a large research interest th…
OccFusion: Multi-Sensor Fusion Framework for 3D Semantic Occupancy Prediction
Zhenxing Ming, Julie Stephany Berrio, Mao Shan +1
A comprehensive understanding of 3D scenes is crucial in autonomous vehicles (AVs), and recent models for 3D semantic occupancy prediction have successfully addressed the challenge…
InverseMatrixVT3D: An Efficient Projection Matrix-Based Approach for 3D Occupancy Prediction
Zhenxing Ming, Julie Stephany Berrio, Mao Shan +1
This paper introduces InverseMatrixVT3D, an efficient method for transforming multi-view image features into 3D feature volumes for 3D semantic occupancy prediction. Existing metho…
Classification of Safety Driver Attention During Autonomous Vehicle Operation
Santiago Gerling Konrad, Julie Stephany Berrio, Mao Shan +2
Despite the continual advances in Advanced Driver Assistance Systems (ADAS) and the development of high-level autonomous vehicles (AV), there is a general consensus that for the sh…
MS3D++: Ensemble of Experts for Multi-Source Unsupervised Domain Adaptation in 3D Object Detection
Darren Tsai, Julie Stephany Berrio, Mao Shan +2
Deploying 3D detectors in unfamiliar domains has been demonstrated to result in a significant 70-90% drop in detection rate due to variations in lidar, geography, or weather from t…
LightFormer: An End-to-End Model for Intersection Right-of-Way Recognition Using Traffic Light Signals and an Attention Mechanism
Zhenxing Ming, Julie Stephany Berrio, Mao Shan +2
For smart vehicles driving through signalised intersections, it is crucial to determine whether the vehicle has right of way given the state of the traffic lights. To address this…
Practical Collaborative Perception: A Framework for Asynchronous and Multi-Agent 3D Object Detection
Minh-Quan Dao, Julie Stephany Berrio, Vincent Frémont +3
Occlusion is a major challenge for LiDAR-based object detection methods. This challenge becomes safety-critical in urban traffic where the ego vehicle must have reliable object det…
MS3D: Leveraging Multiple Detectors for Unsupervised Domain Adaptation in 3D Object Detection
Darren Tsai, Julie Stephany Berrio, Mao Shan +2
We introduce Multi-Source 3D (MS3D), a new self-training pipeline for unsupervised domain adaptation in 3D object detection. Despite the remarkable accuracy of 3D detectors, they o…
Viewer-Centred Surface Completion for Unsupervised Domain Adaptation in 3D Object Detection
Darren Tsai, Julie Stephany Berrio, Mao Shan +2
Every autonomous driving dataset has a different configuration of sensors, originating from distinct geographic regions and covering various scenarios. As a result, 3D detectors te…
Critical concrete scenario generation using scenario-based falsification
Dhanoop Karunakaran, Julie Stephany Berrio, Stewart Worrall +1
Autonomous vehicles have the potential to lower the accident rate when compared to human driving. Moreover, it is the driving force of the automated vehicles' rapid development ove…
Parameterisation of lane-change scenarios from real-world data
Dhanoop Karunakaran, Julie Stephany Berrio, Stewart Worrall +1
Recent Autonomous Vehicles (AV) technology includes machine learning and probabilistic techniques that add significant complexity to the traditional verification and validation met…
Automatic lane change scenario extraction and generation of scenarios in OpenX format from real-world data
Dhanoop Karunakaran, Julie Stephany Berrio, Stewart Worrall +1
Autonomous Vehicles (AV)'s wide-scale deployment appears imminent despite many safety challenges yet to be resolved. The modern autonomous vehicles will undoubtedly include machine…
See Eye to Eye: A Lidar-Agnostic 3D Detection Framework for Unsupervised Multi-Target Domain Adaptation
Darren Tsai, Julie Stephany Berrio, Mao Shan +2
Sampling discrepancies between different manufacturers and models of lidar sensors result in inconsistent representations of objects. This leads to performance degradation when 3D…