39 citations · 62 across the 9 of their papers we have counts for
9 papers
MAPLE: Latent Multi-Agent Play for End-to-End Autonomous Driving
Rajeev Yasarla, Deepti Hegde, Hsin-Pai Cheng +9
Vision-language-action (VLA) models are effective as end-to-end motion planners, but can be brittle when evaluated in closed-loop settings due to being trained under traditional im…
Generative Scenario Rollouts for End-to-End Autonomous Driving
Rajeev Yasarla, Deepti Hegde, Shizhong Han +10
Vision-Language-Action (VLA) models are emerging as highly effective planning models for end-to-end autonomous driving systems. However, current works mostly rely on imitation lear…
Distilling Multi-modal Large Language Models for Autonomous Driving
Deepti Hegde, Rajeev Yasarla, Hong Cai +7
Autonomous driving demands safe motion planning, especially in critical "long-tail" scenarios. Recent end-to-end autonomous driving systems leverage large language models (LLMs) as…
Multimodal 3D Object Detection on Unseen Domains
Deepti Hegde, Suhas Lohit, Kuan-Chuan Peng +2
LiDAR datasets for autonomous driving exhibit biases in properties such as point cloud density, range, and object dimensions. As a result, object detection networks trained and eva…
Equivariant Spatio-Temporal Self-Supervision for LiDAR Object Detection
Deepti Hegde, Suhas Lohit, Kuan-Chuan Peng +2
Popular representation learning methods encourage feature invariance under transformations applied at the input. However, in 3D perception tasks like object localization and segmen…
CLIP goes 3D: Leveraging Prompt Tuning for Language Grounded 3D Recognition
Deepti Hegde, Jeya Maria Jose Valanarasu, Vishal M. Patel
Vision-Language models like CLIP have been widely adopted for various tasks due to their impressive zero-shot capabilities. However, CLIP is not suitable for extracting 3D geometri…
Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection
Deepti Hegde, Vishal M. Patel
3D object detection networks tend to be biased towards the data they are trained on. Evaluation on datasets captured in different locations, conditions or sensors than that of the…
Uncertainty-aware Mean Teacher for Source-free Unsupervised Domain Adaptive 3D Object Detection
Deepti Hegde, Vishwanath Sindagi, Velat Kilic +3
Pseudo-label based self training approaches are a popular method for source-free unsupervised domain adaptation. However, their efficacy depends on the quality of the labels genera…
Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection
Velat Kilic, Deepti Hegde, Vishwanath Sindagi +3
Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars. However, they are known to be sensitive to…