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Safety-Triggered Sliding Mode Tracking Control for A Rehabilitative Training Walker Whose Center of Gravity Markovian Jump Shifts
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DOI：10.1016/j.jfranklin.2026.108739.png)
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Abstract
En 中文
• A primitive-based method named SDCMP is proposed to grade the difficulty of autonomous driving testing scenarios. • Without prior knowledge, HDP-HMM is applied to decompose the scenarios with uncertain V2V interactions into basic primitives. • A gravity model-based method is introduced to quantify the complexities of primitives. • The difficulty of the whole scenario is evaluated according to the proportion of primitives at different complexity levels. • The proposed SDCMP’s grading results are highly in line with the real competition scores, and the consistency reaches 91.3%.
Keywords:
SDCMP
HDP-HMM
V2V interactions
scenario difficulty grading
gravity model
Journal
J
IF:
4.2
Papers:
812
Citations:
0
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