Statistical Risk and Performance Analyses on Naturalistic Driving Trajectory Datasets for Traffic Modeling

The development of autonomous driving technology has made simulation testing one of the most important tools for evaluating system performance. However, there is a lack of systematic methods for analyzing and assessing naturalistic driving trajectory datasets. Specifically, there is a lack of comprehensive analyses on data diversity and balance in machine learning-oriented research. This study presents a comprehensive assessment of existing highway scenario datasets in the context of traffic modeling in autonomous driving simulation tests. In order to clarify the level of traffic risk, we design a systematic risk index and propose an index describing the degree of data scatter based on the principle of Euclidean distance quantization. By comparing several datasets, including NGSIM, highD, INTERACTION, CitySim, and our self-collected Highway dataset, we find that the proposed metrics can effectively quantify the risk level of the dataset while helping to gain insight into the diversity and balance differences of the dataset

Comparative Transcriptomic Analysis Provides Insight into the Key Regulatory Pathways and Differentially Expressed Genes in Blueberry Flower Bud Endo- and Ecodormancy Release

Endodormancy is the stage that perennial plants must go through to prepare for the next seasonal cycle, and it is also an adaptation that allows plants to survive harsh winters. Blueberries (Vaccinium spp.) are known to have high nutritional and commercial value. To better understand the molecular mechanisms of bud dormancy release, the transcriptomes of flower buds from the southern highbush blueberry variety “O’Neal” were analyzed at seven time points of the endo- and ecodormancy release processes. Pairwise comparisons were conducted between adjacent time points; five kinds of phytohormone were identified via these processes. A total of 12,350 differentially expressed genes (DEGs) were obtained from six comparisons. Gene Ontology analysis indicated that these DEGs were significantly involved in metabolic processes and catalytic activity. KEGG pathway analysis showed that these DEGs were predominantly mapped to metabolic pathways and the biosynthesis of secondary metabolites in endodormancy release, but these DEGs were significantly enriched in RNA transport, plant hormone signal transduction, and circadian rhythm pathways in the process of ecodormancy release. The contents of abscisic acid (ABA), salicylic acid (SA), and 1-aminocyclopropane-1-carboxylate (ACC) decreased in endo- and ecodormancy release, and the jasmonic acid (JA) level first decreased in endodormancy release and then increased in ecodormancy release. Weighted correlation network analysis (WGCNA) of transcriptomic data associated with hormone contents generated 25 modules, 9 of which were significantly related to the change in hormone content. The results of this study have important reference value for elucidating the molecular mechanism of flower bud dormancy release