104 citations · 382 across the 38 of their papers we have counts for
43 papers
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…
Virtual Urban Field Studies: Evaluating Urban Interaction Design Using Context-Based Interface Prototypes
Robert Dongas, Kazjon Grace, Samuel Gillespie +3
In this study, we propose the use of virtual urban field studies (VUFS) through context-based interface prototypes for evaluating the interaction design of auditory interfaces. Vir…
Designing Interactions With Shared AVs in Complex Urban Mobility Scenarios
Marius Hoggenmueller, Martin Tomitsch, Stewart Worrall
In this article, we report on the design and evaluation of an external human-machine interface (eHMI) for a real autonomous vehicle (AV), developed to operate as a shared transport…
Context-Based Interface Prototyping: Understanding the Effect of Prototype Representation on User Feedback
Marius Hoggenmueller, Martin Tomitsch, Luke Hespanhol +3
The rise of autonomous systems in cities, such as automated vehicles (AVs), requires new approaches for prototyping and evaluating how people interact with those systems through co…
A Tangible Multi-Display Toolkit to Support the Collaborative Design Exploration of AV-Pedestrian Interfaces
Marius Hoggenmuller, Martin Tomitsch, Callum Parker +4
The advent of cyber-physical systems, such as robots and autonomous vehicles (AVs), brings new opportunities and challenges for the domain of interaction design. Though there is co…
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…
Pedestrian-Vehicle Interaction in Shared Space: Insights for Autonomous Vehicles
Yiyuan Wang, Luke Hespanhol, Stewart Worrall +1
Shared space reduces segregation between vehicles and pedestrians and encourages them to share roads without imposed traffic rules. The behaviour of road users (RUs) is then contro…
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…
Knowledge-aware Graph Transformer for Pedestrian Trajectory Prediction
Yu Liu, Yuexin Zhang, Kunming Li +4
Predicting pedestrian motion trajectories is crucial for path planning and motion control of autonomous vehicles. Accurately forecasting crowd trajectories is challenging due to th…
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…
A Novel Probabilistic V2X Data Fusion Framework for Cooperative Perception
Mao Shan, Karan Narula, Stewart Worrall +4
The paper addresses the vehicle-to-X (V2X) data fusion for cooperative or collective perception (CP). This emerging and promising intelligent transportation systems (ITS) technolog…
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…
What is the appropriate speed for an autonomous vehicle? Designing a Pedestrian Aware Contextual Speed Controller
Daniel Jiang, Stewart Worrall, Mao Shan
Social acceptance is a major hurdle for autonomous vehicle technology, central to which is ensuring both passengers and nearby pedestrians feel safe. This idea of `feeling safe' an…
A Persistent and Context-aware Behavior Tree Framework for Multi Sensor Localization in Autonomous Driving
Siqi Yi, Stewart Worrall, Eduardo Nebot
Robust and persistent localisation is essential for ensuring the safe operation of autonomous vehicles. When operating in large and diverse urban driving environments, autonomous v…