99 citations · 132 across the 10 of their papers we have counts for
12 papers
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…
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…
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…
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…
Optimising the selection of samples for robust lidar camera calibration
Darren Tsai, Stewart Worrall, Mao Shan +2
We propose a robust calibration pipeline that optimises the selection of calibration samples for the estimation of calibration parameters that fit the entire scene. We minimise use…
Demonstrations of Cooperative Perception: Safety and Robustness in Connected and Automated Vehicle Operations
Mao Shan, Karan Narula, Yung Fei Wong +4
Cooperative perception, or collective perception (CP) is an emerging and promising technology for intelligent transportation systems (ITS). It enables an ITS station (ITS-S) to sha…
Long-term map maintenance pipeline for autonomous vehicles
Julie Stephany Berrio, Stewart Worrall, Mao Shan +1
For autonomous vehicles to operate persistently in a typical urban environment, it is essential to have high accuracy position information. This requires a mapping and localisation…
Probabilistic Crowd GAN: Multimodal Pedestrian Trajectory Prediction using a Graph Vehicle-Pedestrian Attention Network
Stuart Eiffert, Kunming Li, Mao Shan +3
Understanding and predicting the intention of pedestrians is essential to enable autonomous vehicles and mobile robots to navigate crowds. This problem becomes increasingly complex…
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…
Probabilistic Egocentric Motion Correction of Lidar Point Cloud and Projection to Camera Images for Moving Platforms
Mao Shan, Julie Stephany Berrio, Stewart Worrall +1
The fusion of sensor data from heterogeneous sensors is crucial for robust perception in various robotics applications that involve moving platforms, for instance, autonomous vehic…