DriveTeach-VLA adds Driving-aware Vision Distillation pretraining and 2D Trajectory-Guided Prompts to VLA models, then reports state-of-the-art results on NAVSIM and nuScenes.
Carllava: Vision language models for camera- only closed-loop driving.ArXiv, abs/2406.10165
10 Pith papers cite this work. Polarity classification is still indexing.
10
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
roles
background 2polarities
background 2representative citing papers
VLGA introduces geometry as a fourth modality in VLA models via pointmap regression loss, reporting SOTA open-loop and closed-loop driving metrics on nuScenes and Bench2Drive.
DVGT-2 is a streaming vision-geometry-action model that jointly reconstructs dense 3D geometry and plans trajectories online, achieving better reconstruction than prior batch methods while transferring directly to planning benchmarks without fine-tuning.
Conditioning speed planning on the predicted path and relabeling synthetic cut-ins yields SOTA Bench2Drive scores (DS 89.07, SR 73.18%).
DriveVLA-W0 adds world modeling to predict future images in VLA models, overcoming sparse action supervision and amplifying data scaling laws on NAVSIM benchmarks and a large in-house dataset.
CogDriver-Agent with sparse temporal memory and spatiotemporal distillation on CogDriver-Data achieves 22% higher closed-loop Driving Score on Bench2Drive and 21% lower mean L2 error on nuScenes.
AutoVLA unifies semantic reasoning and trajectory planning in one autoregressive VLA model for end-to-end autonomous driving by tokenizing trajectories into discrete actions and using GRPO reinforcement fine-tuning to adaptively reduce unnecessary reasoning.
ORION reports 77.74 Driving Score and 54.62% Success Rate on Bench2Drive, outperforming prior end-to-end methods by 14.28 DS and 19.61% SR through unified VQA and planning optimization.
LVDrive improves closed-loop driving on Bench2Drive by adding latent future scene prediction to VLA models via unified embedding space processing and two-stage trajectory decoding.
DeepSight uses parallel latent feature prediction in BEV for long-horizon world modeling and adaptive text reasoning to reach state-of-the-art closed-loop performance on the Bench2drive benchmark.
citing papers explorer
-
Teaching Vision-Language-Action Models What to See and Where to Look
DriveTeach-VLA adds Driving-aware Vision Distillation pretraining and 2D Trajectory-Guided Prompts to VLA models, then reports state-of-the-art results on NAVSIM and nuScenes.
-
VLGA: Vision-Language-Geometry-Action Models for Autonomous Driving
VLGA introduces geometry as a fourth modality in VLA models via pointmap regression loss, reporting SOTA open-loop and closed-loop driving metrics on nuScenes and Bench2Drive.
-
DVGT-2: Vision-Geometry-Action Model for Autonomous Driving at Scale
DVGT-2 is a streaming vision-geometry-action model that jointly reconstructs dense 3D geometry and plans trajectories online, achieving better reconstruction than prior batch methods while transferring directly to planning benchmarks without fine-tuning.
-
AlignDrive: Aligned Lateral-Longitudinal Planning for End-to-End Autonomous Driving
Conditioning speed planning on the predicted path and relabeling synthetic cut-ins yields SOTA Bench2Drive scores (DS 89.07, SR 73.18%).
-
DriveVLA-W0: World Models Amplify Data Scaling Law in Autonomous Driving
DriveVLA-W0 adds world modeling to predict future images in VLA models, overcoming sparse action supervision and amplifying data scaling laws on NAVSIM benchmarks and a large in-house dataset.
-
CogDriver: Integrating Cognitive Inertia for Temporally Coherent Planning in Autonomous Driving
CogDriver-Agent with sparse temporal memory and spatiotemporal distillation on CogDriver-Data achieves 22% higher closed-loop Driving Score on Bench2Drive and 21% lower mean L2 error on nuScenes.
-
AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning
AutoVLA unifies semantic reasoning and trajectory planning in one autoregressive VLA model for end-to-end autonomous driving by tokenizing trajectories into discrete actions and using GRPO reinforcement fine-tuning to adaptively reduce unnecessary reasoning.
-
ORION: A Holistic End-to-End Autonomous Driving Framework by Vision-Language Instructed Action Generation
ORION reports 77.74 Driving Score and 54.62% Success Rate on Bench2Drive, outperforming prior end-to-end methods by 14.28 DS and 19.61% SR through unified VQA and planning optimization.
-
LVDrive: Latent Visual Representation Enhanced Vision-Language-Action Autonomous Driving Model
LVDrive improves closed-loop driving on Bench2Drive by adding latent future scene prediction to VLA models via unified embedding space processing and two-stage trajectory decoding.
-
DeepSight: Long-Horizon World Modeling via Latent States Prediction for End-to-End Autonomous Driving
DeepSight uses parallel latent feature prediction in BEV for long-horizon world modeling and adaptive text reasoning to reach state-of-the-art closed-loop performance on the Bench2drive benchmark.