5 papers
MMCAformer: Macro-Micro Cross-Attention Transformer for Traffic Speed Prediction with Microscopic Connected Vehicle Driving Behavior
Lei Han, Mohamed Abdel-Aty, Younggun Kim +2
Accurate speed prediction is crucial for proactive traffic management to enhance traffic efficiency and safety. Existing studies have primarily relied on aggregated, macroscopic tr…
Multi-view Structural Convolution Network for Domain-Invariant Point Cloud Recognition of Autonomous Vehicles
Younggun Kim, Mohamed Abdel-Aty, Beomsik Cho +2
Point cloud representation has recently become a research hotspot in the field of computer vision and has been utilized for autonomous vehicles. However, adapting deep learning net…
Safe-LLaVA: A Privacy-Preserving Vision-Language Dataset and Benchmark for Biometric Safety
Younggun Kim, Sirnam Swetha, Fazil Kagdi +1
Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in vision-language tasks. However, these models often infer and reveal sensitive biometric attrib…
VRU-Accident: A Vision-Language Benchmark for Video Question Answering and Dense Captioning for Accident Scene Understanding
Younggun Kim, Ahmed S. Abdelrahman, Mohamed Abdel-Aty
Ensuring the safety of vulnerable road users (VRUs), such as pedestrians and cyclists, is a critical challenge for autonomous driving systems, as crashes involving VRUs often resul…
3D Adaptive Structural Convolution Network for Domain-Invariant Point Cloud Recognition
Younggun Kim, Beomsik Cho, Seonghoon Ryoo +1
Adapting deep learning networks for point cloud data recognition in self-driving vehicles faces challenges due to the variability in datasets and sensor technologies, emphasizing t…