852 citations
- Ollscoil na Gaillimhe – University of GalwayIE8 papers
- University of LimerickIE5 papers
- Valeo (France)FR4 papers
- Science Foundation IrelandIE3 papers
- National University of Ireland, MaynoothIE2 papers
- Valeo (Germany)DE2 papers
- Valeo (United States)US2 papers
- Adler PlanetariumUS1 paper
- Akka Technologies (France)FR1 paper
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23 papers
Learnable Quantum Efficiency Filters for Urban Hyperspectral Segmentation
Imad Ali Shah, Jiarong Li, Ethan Delaney +4
Hyperspectral sensing provides rich spectral information for scene understanding in urban driving, but its high dimensionality poses challenges for interpretation and efficient lea…
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…
Hyperspectral Sensors and Autonomous Driving: Technologies, Limitations, and Opportunities
Imad Ali Shah, Jiarong Li, Roshan George +5
Hyperspectral imaging (HSI) offers a transformative sensing modality for Advanced Driver Assistance Systems (ADAS) and autonomous driving (AD) applications, enabling material-level…
Hyperspectral vs. RGB for Pedestrian Segmentation in Urban Driving Scenes: A Comparative Study
Jiarong Li, Imad Ali Shah, Enda Ward +3
Pedestrian segmentation in automotive perception systems faces critical safety challenges due to metamerism in RGB imaging, where pedestrians and backgrounds appear visually indist…
CSNR and JMIM Based Spectral Band Selection for Reducing Metamerism in Urban Driving
Jiarong Li, Imad Ali Shah, Diarmaid Geever +5
Protecting Vulnerable Road Users (VRU) is a critical safety challenge for automotive perception systems, particularly under visual ambiguity caused by metamerism, a phenomenon wher…