Fine-tuning VLMs for driving erodes pre-trained world knowledge, but shifting adaptation to prompt space via the Drive Expert Adapter preserves generalization while improving task performance.
Hilm-d: Towards high-resolution understanding in multimodal large language models for autonomous driv- ing
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
NuRisk is a new VQA dataset for agent-level risk assessment in autonomous driving that benchmarks VLMs at 33% peak accuracy and shows a fine-tuned 7B model reaching 41% with 75% lower latency.
VADv2 introduces a probabilistic planning model that discretizes the high-dimensional action space into tokens, interacts them with scene tokens to predict action distributions, and reports SOTA closed-loop results on CARLA Town05 and Bench2Drive.
UniDrive fuses temporal scene dynamics from video with high-res spatial details via gated cross-attention to jointly generate risk captions and grounded object boxes, outperforming baselines on DRAMA-Reasoning with advantages in small-object detection and zero-shot transfer.
citing papers explorer
-
The Blind Spot of Adaptation: Quantifying and Mitigating Forgetting in Fine-tuned Driving Models
Fine-tuning VLMs for driving erodes pre-trained world knowledge, but shifting adaptation to prompt space via the Drive Expert Adapter preserves generalization while improving task performance.
-
NuRisk: A Visual Question Answering Dataset for Agent-Level Risk Assessment in Autonomous Driving
NuRisk is a new VQA dataset for agent-level risk assessment in autonomous driving that benchmarks VLMs at 33% peak accuracy and shows a fine-tuned 7B model reaching 41% with 75% lower latency.
-
VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning
VADv2 introduces a probabilistic planning model that discretizes the high-dimensional action space into tokens, interacts them with scene tokens to predict action distributions, and reports SOTA closed-loop results on CARLA Town05 and Bench2Drive.
-
UniDrive: A Unified Vision-Language and Grounding Framework for Interpretable Risk Understanding in Autonomous Driving
UniDrive fuses temporal scene dynamics from video with high-res spatial details via gated cross-attention to jointly generate risk captions and grounded object boxes, outperforming baselines on DRAMA-Reasoning with advantages in small-object detection and zero-shot transfer.