91 citations · 152 across the 40 of their papers we have counts for
38 papers · 1 filter
Probing Visual Concepts in Lightweight Vision-Language Models for Automated Driving
Nikos Theodoridis, Reenu Mohandas, Ganesh Sistu +3
The use of Vision-Language Models (VLMs) in automated driving applications is becoming increasingly common, with the aim of leveraging their reasoning and generalisation capabiliti…
Evaluating the Impact of Weather-Induced Sensor Occlusion on BEVFusion for 3D Object Detection
Sanjay Kumar, Tim Brophy, Eoin Martino Grua +3
Accurate 3D object detection is essential for automated vehicles to navigate safely in complex real-world environments. Bird's Eye View (BEV) representations, which project multi-s…
SuperQuadricOcc: Real-Time Self-Supervised Semantic Occupancy Estimation with Superquadric Volume Rendering
Seamie Hayes, Alexandre Boulch, Andrei Bursuc +4
Self-supervision for semantic occupancy estimation is appealing as it removes the labour-intensive manual annotation, thus allowing one to scale to larger autonomous driving datase…
Descriptor: Distance-Annotated Traffic Perception Question Answering (DTPQA)
Nikos Theodoridis, Tim Brophy, Reenu Mohandas +4
The remarkable progress of Vision-Language Models (VLMs) on a variety of tasks has raised interest in their application to automated driving. However, for these models to be truste…
Evaluating Small Vision-Language Models on Distance-Dependent Traffic Perception
Nikos Theodoridis, Tim Brophy, Reenu Mohandas +4
Vision-Language Models (VLMs) are becoming increasingly powerful, demonstrating strong performance on a variety of tasks that require both visual and textual understanding. Their s…
FIN: Fast Inference Network for Map Segmentation
Ruan Bispo, Tim Brophy, Reenu Mohandas +2
Multi-sensor fusion in autonomous vehicles is becoming more common to offer a more robust alternative for several perception tasks. This need arises from the unique contribution of…
PAN: Pillars-Attention-Based Network for 3D Object Detection
Ruan Bispo, Dane Mitrev, Letizia Mariotti +4
Camera-radar fusion offers a robust and low-cost alternative to Camera-lidar fusion for the 3D object detection task in real-time under adverse weather and lighting conditions. How…
Easy3D-Labels: Supervising Semantic Occupancy Estimation with 3D Pseudo-Labels for Automotive Perception
Seamie Hayes, Ganesh Sistu, Tim Brophy +1
In perception for automated vehicles, safety is critical not only for the driver but also for other agents in the scene, particularly vulnerable road users such as pedestrians and…
BEVMOSNet: Multimodal Fusion for BEV Moving Object Segmentation
Hiep Truong Cong, Ajay Kumar Sigatapu, Arindam Das +4
Accurate motion understanding of the dynamic objects within the scene in bird's-eye-view (BEV) is critical to ensure a reliable obstacle avoidance system and smooth path planning f…
Image Segmentation: Inducing graph-based learning
Aryan Singh, Pepijn Van de Ven, Ciarán Eising +1
This study explores the potential of graph neural networks (GNNs) to enhance semantic segmentation across diverse image modalities. We evaluate the effectiveness of a novel GNN-bas…
Revisiting Birds Eye View Perception Models with Frozen Foundation Models: DINOv2 and Metric3Dv2
Seamie Hayes, Ganesh Sistu, Ciarán Eising
Birds Eye View perception models require extensive data to perform and generalize effectively. While traditional datasets often provide abundant driving scenes from diverse locatio…
Minimizing Occlusion Effect on Multi-View Camera Perception in BEV with Multi-Sensor Fusion
Sanjay Kumar, Hiep Truong, Sushil Sharma +4
Autonomous driving technology is rapidly evolving, offering the potential for safer and more efficient transportation. However, the performance of these systems can be significantl…
Reflective Teacher: Semi-Supervised Multimodal 3D Object Detection in Bird's-Eye-View via Uncertainty Measure
Saheli Hazra, Sudip Das, Rohit Choudhary +4
Applying pseudo labeling techniques has been found to be advantageous in semi-supervised 3D object detection (SSOD) in Bird's-Eye-View (BEV) for autonomous driving, particularly wh…
Deformable Convolution Based Road Scene Semantic Segmentation of Fisheye Images in Autonomous Driving
Anam Manzoor, Aryan Singh, Ganesh Sistu +4
This study investigates the effectiveness of modern Deformable Convolutional Neural Networks (DCNNs) for semantic segmentation tasks, particularly in autonomous driving scenarios w…
SS-SFR: Synthetic Scenes Spatial Frequency Response on Virtual KITTI and Degraded Automotive Simulations for Object Detection
Daniel Jakab, Alexander Braun, Cathaoir Agnew +6
Automotive simulation can potentially compensate for a lack of training data in computer vision applications. However, there has been little to no image quality evaluation of autom…
Subgraph Clustering and Atom Learning for Improved Image Classification
Aryan Singh, Pepijn Van de Ven, Ciarán Eising +1
In this study, we present the Graph Sub-Graph Network (GSN), a novel hybrid image classification model merging the strengths of Convolutional Neural Networks (CNNs) for feature ext…
MapsTP: HD Map Images Based Multimodal Trajectory Prediction for Automated Vehicles
Sushil Sharma, Arindam Das, Ganesh Sistu +2
Predicting ego vehicle trajectories remains a critical challenge, especially in urban and dense areas due to the unpredictable behaviours of other vehicles and pedestrians. Multimo…
Optimizing Visual Question Answering Models for Driving: Bridging the Gap Between Human and Machine Attention Patterns
Kaavya Rekanar, Martin Hayes, Ganesh Sistu +1
Visual Question Answering (VQA) models play a critical role in enhancing the perception capabilities of autonomous driving systems by allowing vehicles to analyze visual inputs alo…
Surround-View Fisheye Optics in Computer Vision and Simulation: Survey and Challenges
Daniel Jakab, Brian Michael Deegan, Sushil Sharma +6
In this paper, we provide a survey on automotive surround-view fisheye optics, with an emphasis on the impact of optical artifacts on computer vision tasks in autonomous driving an…
Fisheye Camera and Ultrasonic Sensor Fusion For Near-Field Obstacle Perception in Bird's-Eye-View
Arindam Das, Sudarshan Paul, Niko Scholz +4
Accurate obstacle identification represents a fundamental challenge within the scope of near-field perception for autonomous driving. Conventionally, fisheye cameras are frequently…
Measuring Natural Scenes SFR of Automotive Fisheye Cameras
Daniel Jakab, Eoin Martino Grua, Brian Micheal Deegan +3
The Modulation Transfer Function (MTF) is an important image quality metric typically used in the automotive domain. However, despite the fact that optical quality has an impact on…
Optimizing Ego Vehicle Trajectory Prediction: The Graph Enhancement Approach
Sushil Sharma, Aryan Singh, Ganesh Sistu +2
Predicting the trajectory of an ego vehicle is a critical component of autonomous driving systems. Current state-of-the-art methods typically rely on Deep Neural Networks (DNNs) an…
BEVSeg2TP: Surround View Camera Bird's-Eye-View Based Joint Vehicle Segmentation and Ego Vehicle Trajectory Prediction
Sushil Sharma, Arindam Das, Ganesh Sistu +2
Trajectory prediction is, naturally, a key task for vehicle autonomy. While the number of traffic rules is limited, the combinations and uncertainties associated with each agent's…
Self-Supervised Online Camera Calibration for Automated Driving and Parking Applications
Ciarán Hogan, Ganesh Sistu, Ciarán Eising
Camera-based perception systems play a central role in modern autonomous vehicles. These camera based perception algorithms require an accurate calibration to map the real world di…