6 papers
Partition the Support, Reconstruct the Residual: Training-Free Sparse Attention for Video Generation and World Models
Pardis Taghavi, Reza Langari, Gaurav Pandey
Training-free block-sparse attention can accelerate video transformers, but row-wise attention concentration does not by itself specify an executable sparse operator. Queries shari…
ReaLiTy and LADS: A Unified Framework and Dataset Suite for LiDAR Adaptation Across Sensors and Adverse Weather Conditions
Vivek Anand, Bharat Lohani, Rakesh Mishra +1
Reliable LiDAR perception requires robustness across sensors, environments, and adverse weather. However, existing datasets rarely provide physically consistent observations of the…
Simulating Realistic LiDAR Data Under Adverse Weather for Autonomous Vehicles: A Physics-Informed Learning Approach
Vivek Anand, Bharat Lohani, Rakesh Mishra +1
Accurate LiDAR simulation is crucial for autonomous driving, especially under adverse weather conditions. Existing methods struggle to capture the complex interactions between LiDA…
NaviDriveVLM: Decoupling High-Level Reasoning and Motion Planning for Autonomous Driving
Ximeng Tao, Pardis Taghavi, Dimitar Filev +2
Vision-language models (VLMs) have emerged as a promising direction for end-to-end autonomous driving (AD) by jointly modeling visual observations, driving context, and language-ba…
Toward Unified Multimodal Representation Learning for Autonomous Driving
Ximeng Tao, Dimitar Filev, Gaurav Pandey
Contrastive Language-Image Pre-training (CLIP) has shown impressive performance in aligning visual and textual representations. Recent studies have extended this paradigm to 3D vis…
Learning Autonomy: Off-Road Navigation Enhanced by Human Input
Akhil Nagariya, Dimitar Filev, Srikanth Saripalli +1
In the area of autonomous driving, navigating off-road terrains presents a unique set of challenges, from unpredictable surfaces like grass and dirt to unexpected obstacles such as…