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arXiv Computer Vision By Ruoyu Yao, Yusen Xie, Qingzhao Liu, Pei Liu, Zewei Yang, Yipeng Zhu, Xiaolong Wang, Jun Ma

Continuous Actions from Discrete Minds: Latent-Aligned Planning for End-to-End Autonomous Driving

Read the original on arXiv Computer Vision →

The paper introduces LaPla, a Vision‑Language‑Action framework that uses a latent‑aligned planning approach to convert discrete semantic reasoning into continuous, physics‑constrained driving actions. It employs a residual VQ‑VAE to encode vehicle kinematics into a structured latent space, then projects multimodal inputs—images, past actions, and text—directly into this latent space, allowing a frozen decoder to generate physically plausible trajectories without quantization errors. Experiments on nuScenes and NVIDIA AlpaSim show LaPla reduces long‑horizon L2 error by 15.52% and improves closed‑loop success rates by 33.34 percentage points while cutting inference latency.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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