5 papers
See Less, Drive Better: Generalizable End-to-End Autonomous Driving via Foundation Models Stochastic Patch Selection
Amir Mallak, Erfan Aasi, Shiva Sreeram +3
Recent advances in end-to-end autonomous driving show that policies trained on patch-aligned features extracted from foundation models generalize better to Out-of-Distribution (OOD…
SAFe-Copilot: Unified Shared Autonomy Framework
Phat Nguyen, Erfan Aasi, Shiva Sreeram +4
Autonomous driving systems remain brittle in rare, ambiguous, and out-of-distribution scenarios, where human driver succeed through contextual reasoning. Shared autonomy has emerge…
Compress to Impress: Efficient LLM Adaptation Using a Single Gradient Step on 100 Samples
Shiva Sreeram, Alaa Maalouf, Pratyusha Sharma +1
Recently, Sharma et al. suggested a method called Layer-SElective-Rank reduction (LASER) which demonstrated that pruning high-order components of carefully chosen LLM's weight matr…
Generating Out-Of-Distribution Scenarios Using Language Models
Erfan Aasi, Phat Nguyen, Shiva Sreeram +3
The deployment of autonomous vehicles controlled by machine learning techniques requires extensive testing in diverse real-world environments, robust handling of edge cases and out…
Probing Multimodal LLMs as World Models for Driving
Shiva Sreeram, Tsun-Hsuan Wang, Alaa Maalouf +3
We provide a sober look at the application of Multimodal Large Language Models (MLLMs) in autonomous driving, challenging common assumptions about their ability to interpret dynami…