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QuantV2X system achieves 3.2x lower latency in vehicle perception

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced QuantV2X, a novel multi-agent system designed for efficient cooperative perception in vehicles. This system utilizes full quantization for both neural network models and transmitted messages, significantly reducing computational and transmission costs without sacrificing accuracy. QuantV2X achieves a 3.2x reduction in system-level latency and a notable improvement in mAP30 compared to full-precision systems, making it more suitable for real-time, resource-constrained environments. AI

IMPACT Enables more efficient and deployable AI systems for real-time vehicle perception.

RANK_REASON Research paper introducing a new system and methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

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

QuantV2X system achieves 3.2x lower latency in vehicle perception

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Research paper introducing a new system and methodology. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Seth Z. Zhao, Huizhi Zhang, Zhaowei Li, Juntong Peng, Anthony Chui, Zewei Zhou, Zonglin Meng, Hao Xiang, Zhiyu Huang, Fujia Wang, Ran Tian, Chenfeng Xu, Bolei Zhou, Jiaqi Ma ·

    QuantV2X: A Fully Quantized Multi-Agent System for Cooperative Perception

    arXiv:2509.03704v2 Announce Type: replace Abstract: Cooperative perception through Vehicle-to-Everything (V2X) communication offers significant potential for enhancing vehicle perception by mitigating occlusions and expanding the field of view. However, past research has predomin…

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