TOFG: A Unified and Fine-Grained Environment Representation in Autonomous Driving

In autonomous driving, an accurate understanding of environment, e.g., the vehicle-to-vehicle and vehicle-to-lane interactions, plays a critical role in many driving tasks such as trajectory prediction and motion planning. Environment information comes from high-definition (HD) map and historical trajectories of vehicles. Due to the heterogeneity of the map data and trajectory data, many data-driven models for trajectory prediction and motion planning extract vehicle-to-vehicle and vehicle-to-lane interactions in a separate and sequential manner. However, such a manner may capture biased interpretation of interactions, causing lower prediction and planning accuracy. Moreover, separate extraction leads to a complicated model structure and hence the overall efficiency and scalability are sacrificed. To address the above issues, we propose an environment representation, Temporal Occupancy Flow Graph (TOFG). Specifically, the occupancy flow-based representation unifies the map information and vehicle trajectories into a homogeneous data format and enables a consistent prediction. The temporal dependencies among vehicles can help capture the change of occupancy flow timely to further promote model performance. To demonstrate that TOFG is capable of simplifying the model architecture, we incorporate TOFG with a simple graph attention (GAT) based neural network and propose TOFG-GAT, which can be used for both trajectory prediction and motion planning. Experiment results show that TOFG-GAT achieves better or competitive performance than all the SOTA baselines with less training time.Comment: Accepted by ICRA 202

Understanding Travel Behavior and Accessibility for Older Adults: A Comprehensive Framework

This study used a mixed-method design to examine travel behavior and accessibility of older adults. The research team conducted a statewide survey and focus groups to gather travel behavior data of older adults (50+) residing in Utah. The study also employed a two-step floating catchment area method, a novel spatial technique, and integrated the survey data to accurately measure travel accessibility of older adults. Using the survey data of 724 older adults as well as the focus group interviews of 18 older individuals, we found a significant dissatisfaction and vulnerability experienced by older adults with limited mobility. The distribution patterns of accessibility revealed communities with limited options for specific types of facilities, highlighting the need for addressing equitable access to different destinations. The study identified a positive relationship between travel frequency and satisfaction up to a certain threshold, beyond which satisfaction declined. Further investigation is needed to explore this threshold, considering health related issues and travel fatigue. Lastly, the study emphasized the importance of considering diverse dimensions of older adults\u27 needs and developing distinct accessibility measures for underrepresented groups, such as those with low income, disabilities, and older adults experiencing mobility limitations. The findings highlight the need for policymakers to address the critical accessibility and mobility gaps and improve travel experiences for older adults

High-efficient screening method for identification of key genes in breast cancer through microarray and bioinformatics

Background/Aim: The aim of the present study was to identify key pathways and genes in breast cancer and develop a new method for screening key genes with abnormal expression based on bioinformatics. Materials and Methods: Three microarray datasets GSE21422, GSE42568 and GSE45827 were downloaded from the Gene Expression Omnibus (GEO) database and differentially expressed genes (DEGs) were analyzed using GEO2R. The gene ontology (GO) and pathway enrichment analysis were established through DAVID database. The protein–protein interaction (PPI) network was performed through the Search Tool for the Retrieval of Interacting Genes (STRING) database and managed by Cytoscape. The overall survival (OS) analysis of the 4 genes including AURKA, CDH1, CDK1 and PPARG that had higher degrees in this network was uncovered Kaplan-Meier analysis. Results: A total of 811 DEGs were identified in breast cancer, which were enriched in biological processes, including cell cycle, mitosis, vessel development and lipid metabolic. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that the up-regulated DEGs were particularly involved in cell cycle, progesterone-mediated oocyte maturation and leukocyte transendothelial migration, while the down-regulated DEGs were mainly involved in regulation of lipolysis, fatty acid degradation and glycerolipid metabolism. Through PPI network analysis, 14 hub genes were identified. Among them, the high expression of AURKA, CDH1 and CDK1 were associated with worse OS of breast cancer patients; while the high expression of PPARG was linked with better OS. Conclusion: The present study identified key pathways and genes involved in breast cancer which are potential molecular targets for breast cancer treatment and diagnosis