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cs.CV2026

RT-VLA: Real-Time Vision-Language-Action Models via Knowledge Distillation

Xiangyu Huang, Zhenlin Hua, Han Zhou +2

Vision-Language-Action (VLA) models have shown strong potential for end-to-end autonomous driving by jointly modeling visual perception, language reasoning, explainability and acti…

cs.CV2026

BEVMAPMATCH: Multimodal BEV Neural Map Matching for Robust Re-Localization of Autonomous Vehicles

Shounak Sural, Ragunathan Rajkumar

Localization in GNSS-denied and GNSS-degraded environments is a challenge for the safe widespread deployment of autonomous vehicles. Such GNSS-challenged environments require alter…

cs.CV2025

ObjectTransforms for Uncertainty Quantification and Reduction in Vision-Based Perception for Autonomous Vehicles

Nishad Sahu, Shounak Sural, Aditya Satish Patil +2

Reliable perception is fundamental for safety critical decision making in autonomous driving. Yet, vision based object detector neural networks remain vulnerable to uncertainty ari…

cs.CV2024

ContextVLM: Zero-Shot and Few-Shot Context Understanding for Autonomous Driving using Vision Language Models

Shounak Sural, Naren, Ragunathan Rajkumar

In recent years, there has been a notable increase in the development of autonomous vehicle (AV) technologies aimed at improving safety in transportation systems. While AVs have be…

cs.CV2024

ContextualFusion: Context-Based Multi-Sensor Fusion for 3D Object Detection in Adverse Operating Conditions

Shounak Sural, Nishad Sahu, Ragunathan Rajkumar

The fusion of multimodal sensor data streams such as camera images and lidar point clouds plays an important role in the operation of autonomous vehicles (AVs). Robust perception a…