1 citations · 1 across the 11 of their papers we have counts for
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Counterfactual VLA: Self-Reflective Vision-Language-Action Model with Adaptive Reasoning
Zhenghao "Mark" Peng, Wenhao Ding, Yurong You +11
Recent reasoning-augmented Vision-Language-Action (VLA) models have improved the interpretability of end-to-end autonomous driving by generating intermediate reasoning traces. Yet…
dVLM-AD: Enhance Diffusion Vision-Language-Model for Driving via Controllable Reasoning
Yingzi Ma, Yulong Cao, Wenhao Ding +6
The autonomous driving community is increasingly focused on addressing the challenges posed by out-of-distribution (OOD) driving scenarios. A dominant research trend seeks to enhan…
Latent Chain-of-Thought World Modeling for End-to-End Driving
Shuhan Tan, Kashyap Chitta, Yuxiao Chen +8
Recent Vision-Language-Action (VLA) models for autonomous driving explore inference-time reasoning as a way to improve driving performance and safety in challenging scenarios. Most…
Alpamayo-R1: Bridging Reasoning and Action Prediction for Generalizable Autonomous Driving in the Long Tail
NVIDIA, :, Yan Wang +41
End-to-end architectures trained via imitation learning have advanced autonomous driving by scaling model size and data, yet performance remains brittle in safety-critical long-tai…
RealDrive: Retrieval-Augmented Driving with Diffusion Models
Wenhao Ding, Sushant Veer, Yuxiao Chen +3
Learning-based planners generate natural human-like driving behaviors by learning to reason about nuanced interactions from data, overcoming the rigid behaviors that arise from rul…
Surprise Potential as a Measure of Interactivity in Driving Scenarios
Wenhao Ding, Sushant Veer, Karen Leung +2
Validating the safety and performance of an autonomous vehicle (AV) requires benchmarking on real-world driving logs. However, typical driving logs contain mostly uneventful scenar…