3 citations · 5 across the 12 of their papers we have counts for
12 papers
DriveCtrl: Conditioned Sim-to-Real Driving Video Generation
Haonan Zhao, Yiting Wang, Jingkun Chen +3
Large-scale labelled driving video data is essential for training autonomous driving systems. Although simulation offers scalable and fully annotated data, the domain gap between s…
Adverse-to-the-eXtreme Panoptic Segmentation: URVIS 2026 Study and Benchmark
Yiting Wang, Nolwenn Peyratout, Tim Brodermann +13
This paper presents the report of the URVIS 2026 challenge on adverse-to-extreme panoptic segmentation. As the first challenge of its kind, it attracted 17 registered participants…
AURORA-KITTI: Any-Weather Depth Completion and Denoising
Yiting Wang, Tim Brödermann, Hamed Haghighi +4
Robust depth completion is fundamental to real-world 3D scene understanding, yet existing RGB--LiDAR fusion methods degrade significantly under adverse weather, where both camera i…
CapHDR2IR: Caption-Driven Transfer from Visible Light to Infrared Domain
Jingchao Peng, Thomas Bashford-Rogers, Zhuang Shao +4
Infrared (IR) imaging offers advantages in several fields due to its unique ability of capturing content in extreme light conditions. However, the demanding hardware requirements o…
Luminance Component Analysis for Exposure Correction
Jingchao Peng, Thomas Bashford-Rogers, Jingkun Chen +3
Exposure correction methods aim to adjust the luminance while maintaining other luminance-unrelated information. However, current exposure correction methods have difficulty in ful…
Good Data Is All Imitation Learning Needs
Amir Samadi, Konstantinos Koufos, Kurt Debattista +1
In this paper, we address the limitations of traditional teacher-student models, imitation learning, and behaviour cloning in the context of Autonomous/Automated Driving Systems (A…
SAFE-RL: Saliency-Aware Counterfactual Explainer for Deep Reinforcement Learning Policies
Amir Samadi, Konstantinos Koufos, Kurt Debattista +1
While Deep Reinforcement Learning (DRL) has emerged as a promising solution for intricate control tasks, the lack of explainability of the learned policies impedes its uptake in sa…
Exploring Generative AI for Sim2Real in Driving Data Synthesis
Haonan Zhao, Yiting Wang, Thomas Bashford-Rogers +2
Datasets are essential for training and testing vehicle perception algorithms. However, the collection and annotation of real-world images is time-consuming and expensive. Driving…
Taming Transformers for Realistic Lidar Point Cloud Generation
Hamed Haghighi, Amir Samadi, Mehrdad Dianati +2
Diffusion Models (DMs) have achieved State-Of-The-Art (SOTA) results in the Lidar point cloud generation task, benefiting from their stable training and iterative refinement during…
Benchmarking the Robustness of Panoptic Segmentation for Automated Driving
Yiting Wang, Haonan Zhao, Daniel Gummadi +3
Precise situational awareness is required for the safe decision-making of assisted and automated driving (AAD) functions. Panoptic segmentation is a promising perception technique…
A Unified Generative Framework for Realistic Lidar Simulation in Autonomous Driving Systems
Hamed Haghighi, Mehrdad Dianati, Valentina Donzella +1
Simulation models for perception sensors are integral components of automotive simulators used for the virtual Verification and Validation (V\&V) of Autonomous Driving Systems (ADS…
Counterfactual Explainer Framework for Deep Reinforcement Learning Models Using Policy Distillation
Amir Samadi, Konstantinos Koufos, Kurt Debattista +1
Deep Reinforcement Learning (DRL) has demonstrated promising capability in solving complex control problems. However, DRL applications in safety-critical systems are hindered by th…