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cs.RO2025

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…

cs.CV2025

Towards Efficient and Effective Multi-Camera Encoding for End-to-End Driving

Jiawei Yang, Ziyu Chen, Yurong You +7

We present Flex, an efficient and effective scene encoder that addresses the computational bottleneck of processing high-volume multi-camera data in end-to-end autonomous driving.…

cs.CV2025

FoundationMotion: Auto-Labeling and Reasoning about Spatial Movement in Videos

Yulu Gan, Ligeng Zhu, Dandan Shan +8

Motion understanding is fundamental to physical reasoning, enabling models to infer dynamics and predict future states. However, state-of-the-art models still struggle on recent mo…

cs.CV2025

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…

cs.RO2025

RoaD: Rollouts as Demonstrations for Closed-Loop Supervised Fine-Tuning of Autonomous Driving Policies

Guillermo Garcia-Cobo, Maximilian Igl, Peter Karkus +5

Autonomous driving policies are typically trained via open-loop behavior cloning of human demonstrations. However, such policies suffer from covariate shift when deployed in closed…

cs.RO2025

Trends in Motion Prediction Toward Deployable and Generalizable Autonomy: A Revisit and Perspectives

Letian Wang, Marc-Antoine Lavoie, Sandro Papais +13

Motion prediction, recently popularized as world models, refers to the anticipation of future agent states or scene evolution, which is rooted in human cognition, bridging percepti…