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This paper formulates subtle GNSS spoofing detection as a causal sequential evidence-modeling problem and proposes a high-order liquid evidence detector that achieves the lowest false-positive rate among the learning-based baselines.
Urban transportation systems generate heterogeneous data, yet these data do not automatically become actionable management intelligence. This chapter adopts a behavior-centered perspective on artificial intelligence (AI), treating mobility records and passenger-generated text as behavioral evidence rather than behavior...
A causal high-order liquid evidence framework for GNSS spoofing detection that achieves the highest F1-scores among the evaluated temporal models on Dataset~1 and Dataset~3, and detects both labeled normal-to-attack transitions within four sampling steps.
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