104 citations · 146 across the 27 of their papers we have counts for
20 papers · 1 filter
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…
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…
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…
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…
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…
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…
Camera-Lidar Integration: Probabilistic sensor fusion for semantic mapping
Julie Stephany Berrio, Mao Shan, Stewart Worrall +1
An automated vehicle operating in an urban environment must be able to perceive and recognise object/obstacles in a three-dimensional world while navigating in a constantly changin…
Automatic extrinsic calibration between a camera and a 3D Lidar using 3D point and plane correspondences
Surabhi Verma, Julie Stephany Berrio, Stewart Worrall +1
This paper proposes an automated method to obtain the extrinsic calibration parameters between a camera and a 3D lidar with as low as 16 beams. We use a checkerboard as a reference…
Automated Evaluation of Semantic Segmentation Robustness for Autonomous Driving
Wei Zhou, Julie Stephany Berrio, Stewart Worrall +1
One of the fundamental challenges in the design of perception systems for autonomous vehicles is validating the performance of each algorithm under a comprehensive variety of opera…