Person2Drive is a new benchmark that generates personalized driving datasets via simulation, quantifies styles with MMD and KL metrics, and adapts E2E-AD models using a style reward framework.
In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
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SpanVLA reduces action generation latency via flow-matching conditioned on history and improves robustness by training on negative-recovery samples with GRPO and a dedicated reasoning dataset.
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Driving with A Thousand Faces: A Benchmark for Closed-Loop Personalized End-to-End Autonomous Driving
Person2Drive is a new benchmark that generates personalized driving datasets via simulation, quantifies styles with MMD and KL metrics, and adapts E2E-AD models using a style reward framework.
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SpanVLA: Efficient Action Bridging and Learning from Negative-Recovery Samples for Vision-Language-Action Model
SpanVLA reduces action generation latency via flow-matching conditioned on history and improves robustness by training on negative-recovery samples with GRPO and a dedicated reasoning dataset.