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.
Drivegpt4: Interpretable end-to-end autonomous driving via large language model.IEEE Robotics and Automation Letters
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Adversarial patches transfer across three VLM architectures in autonomous driving scenarios with 73-91% success rates and affect 65-79% of critical decision frames even without target-specific optimization.
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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.
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Understanding Adversarial Transferability in Vision-Language Models for Autonomous Driving: A Cross-Architecture Analysis
Adversarial patches transfer across three VLM architectures in autonomous driving scenarios with 73-91% success rates and affect 65-79% of critical decision frames even without target-specific optimization.