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.
Trajeglish: Traffic modeling as next-token prediction
3 Pith papers cite this work. Polarity classification is still indexing.
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CRAFT reduces collisions by 31.2% and traffic violations by 33.2% in closed-loop traffic simulation by discovering context-induced failures in what-if rollouts and using a contextual preference evaluator to reweight autoregressive decoding toward globally coherent behaviors.
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
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Bridging Local Observation and Global Simulation in Closed-Loop Traffic Modeling
CRAFT reduces collisions by 31.2% and traffic violations by 33.2% in closed-loop traffic simulation by discovering context-induced failures in what-if rollouts and using a contextual preference evaluator to reweight autoregressive decoding toward globally coherent behaviors.
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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.