Robotics & Machine Learning Daily News2024,Issue(MAY.10) :27-28.

Findings from Jilin University Reveals New Findings on Intelligent Transport Sys tems (An Entropy-based Model for Quantifying Multidimensional Traffic Scenario Complexity)

Robotics & Machine Learning Daily News2024,Issue(MAY.10) :27-28.

Findings from Jilin University Reveals New Findings on Intelligent Transport Sys tems (An Entropy-based Model for Quantifying Multidimensional Traffic Scenario Complexity)

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Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News - New research on Transportation - Intelligent Tran sport Systems is the subject of a report. According to news reporting out of Cha ngchun, People’s Republic of China, by NewsRx editors, research stated, “Quantif ying the complexity of traffic scenarios not only provides an essential foundati on for constructing the scenarios used in autonomous vehicle training and testin g, but also enhances the robustness of the resulting driving decisions and plann ing operations. However, currently available quantification methods suffer from inaccuracies and coarse-granularity in complexity measurements due to issues suc h as insufficient specificity or indirect quantification.”

Key words

Changchun/People’s Republic of China/A sia/Intelligent Transport Systems/Transportation/Jilin University

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
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