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STELLAR model advances autonomous driving perception with 3D data fusion

By PulseAugur Editorial · [1 sources] ·

Researchers have developed STELLAR, a new large model for 3D perception in autonomous driving, by extending a Sparse Window Transformer to integrate LiDAR, radar, camera, and map data. Trained on 50 million driving examples with up to 500 million parameters, the model establishes a new state-of-the-art on the Waymo Open Dataset. The study demonstrates that scaling models with large datasets and compute is a viable path for advancing autonomous driving perception systems. AI

IMPACT Establishes new state-of-the-art in autonomous driving perception, demonstrating the effectiveness of large-scale training for complex 3D data fusion.

RANK_REASON The cluster describes a new academic paper detailing a novel model architecture and its performance on a benchmark dataset. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

STELLAR model advances autonomous driving perception with 3D data fusion

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The cluster describes a new academic paper detailing a novel model architecture and its performance on a benchmark dataset. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Yingwei Li, Xin Huang, Yang Liu, Yang Fu, Alex Zihao Zhu, Chen Song, Junwen Yao, Anant Subramanian, Hao Xiang, Weijing Shi, Yuliang Zou, Tom Hoddes, Zhaoqi Leng, Govind Thattai, Dragomir Anguelov, Mingxing Tan ·

    STELLAR: Scaling 3D Perception Large Models for Autonomous Driving

    arXiv:2605.20390v1 Announce Type: cross Abstract: Model scaling has demonstrated remarkable success through large-scale training on diverse datasets. It remains an open question whether the same paradigm would apply to autonomous driving perception systems due to unique challenge…

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