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Updated: Jan 10, 2026

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不确定性加权的多任务学习,以实现可靠的交通场景语义理解.

Zhiping Wan1, Shitong Ye2, Feng Wang1

  • 1School of Information and Intelligence Engineering, Guangzhou Xinhua University, Dongguan, 523133, China.

Scientific reports
|November 20, 2025
PubMed
概括

本研究引入了一个不确定性加权的多任务学习框架 (UW-MTL),以提高在天气和堵塞等不利条件下对交通场景的理解. 这种新的方法显著改善了感知任务,特别是在具有挑战性的场景中.

关键词:
BEV 代表性的代表性专家组合 变压器专家组合多任务学习是多任务学习.对交通场景的语义理解.不确定性的加权.

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