26 citations · 27 across the 10 of their papers we have counts for
11 papers
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…
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…
SOLAS: Superpositioning an Optical Lens in Automotive Simulation
Daniel Jakab, Julian Barthel, Alexander Braun +7
Automotive Simulation is a potentially cost-effective strategy to identify and test corner case scenarios in automotive perception. Recent work has shown a significant shift in cre…
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…
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…
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…
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…