Search Results for author:
Found 17 papers, 8 papers with code
MS3D++: Ensemble of Experts for Multi-Source Unsupervised Domain Adaption in 3D Object Detection
MS3D++ provides a straightforward approach to domain adaptation by generating high-quality pseudo-labels, enabling the adaptation of 3D detectors to a diverse range of lidar types, regardless of their density.
LightFormer: An End-to-End Model for Intersection Right-of-Way Recognition Using Traffic Light Signals and an Attention Mechanism
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.
MS3D: Leveraging Multiple Detectors for Unsupervised Domain Adaptation in 3D Object Detection
Our proposed Kernel-Density Estimation (KDE) Box Fusion method fuses box proposals from multiple domains to obtain pseudo-labels that surpass the performance of the best source domain detectors.
Viewer-Centred Surface Completion for Unsupervised Domain Adaptation in 3D Object Detection
With SEE-VCN, we obtain a unified representation of objects across datasets, allowing the network to focus on learning geometry, rather than overfitting on scan patterns.
See Eye to Eye: A Lidar-Agnostic 3D Detection Framework for Unsupervised Multi-Target Domain Adaptation
Sampling discrepancies between different manufacturers and models of lidar sensors result in inconsistent representations of objects.
Optimising the selection of samples for robust lidar camera calibration
We propose a robust calibration pipeline that optimises the selection of calibration samples for the estimation of calibration parameters that fit the entire scene.
Socially Aware Crowd Navigation with Multimodal Pedestrian Trajectory Prediction for Autonomous Vehicles
Seamlessly operating an autonomous vehicle in a crowded pedestrian environment is a very challenging task.
Robotics
Attentional-GCNN: Adaptive Pedestrian Trajectory Prediction towards Generic Autonomous Vehicle Use Cases
Autonomous vehicle navigation in shared pedestrian environments requires the ability to predict future crowd motion both accurately and with minimal delay.
Efficient falsification approach for autonomous vehicle validation using a parameter optimisation technique based on reinforcement learning
The approach is based on a parameter optimisation problem to search for challenging scenarios.
Camera-Lidar Integration: Probabilistic sensor fusion for semantic mapping
Our approach is capable of using a multi-sensor platform to build a three-dimensional semantic voxelized map that considers the uncertainty of all of the processes involved.
Probabilistic Crowd GAN: Multimodal Pedestrian Trajectory Prediction using a Graph Vehicle-Pedestrian Attention Network
Understanding and predicting the intention of pedestrians is essential to enable autonomous vehicles and mobile robots to navigate crowds.
Efficient statistical validation with edge cases to evaluate Highly Automated Vehicles
The widescale deployment of Autonomous Vehicles (AV) seems to be imminent despite many safety challenges that are yet to be resolved.
Semantic sensor fusion: from camera to sparse lidar information
This paper presents an approach to fuse different sensory information, Light Detection and Ranging (lidar) scans and camera images.
Automatic extrinsic calibration between a camera and a 3D Lidar using 3D point and plane correspondences
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.
Automated Evaluation of Semantic Segmentation Robustness for Autonomous Driving
This paper presents a novel method for analysing the robustness of semantic segmentation models and provides a number of metrics to evaluate the classification performance over a variety of environmental conditions.
Adapting Semantic Segmentation Models for Changes in Illumination and Camera Perspective
Semantic segmentation using deep neural networks has been widely explored to generate high-level contextual information for autonomous vehicles.
Naturalistic Driver Intention and Path Prediction using Recurrent Neural Networks
Understanding the intentions of drivers at intersections is a critical component for autonomous vehicles.
