Accurate assessment of human trust in driver assistance systems is crucial for enhancing user acceptance and system safety. While prior research has explored physiological signals like skin conductance and heart rate to gauge trust, the link between these signals and trust remains insufficiently understood. Here, we present a novel approach to interpreting driver trust in intelligent warning systems by integrating subjective measures from questionnaires with objective electroencephalography (EEG) data. We develop TrustNet, a model leveraging separable convolution to capture the spatiotemporal dynamics of EEG signals and class activation mapping (CAM) to identify trust-relevant features. TrustNet achieves superior performance in trust assessment and classification, with accuracy and F1 score both exceeding 87%. CAM analysis reveals that EEG beta- and gamma-wave changes in the occipital and frontal regions are strongly associated with trust dynamics. Misclassification analysis highlights sensor noise and individual differences in response variability as key factors affecting performance. These findings demonstrate the feasibility of EEG-based trust assessment, offering new avenues for adaptive driver assistance systems responsive to human trust.

Risk assessment plays an essential role in the improvement of driving safety for intelligent vehicles. Current methods ignoring the predictive and personalized impact of driving policies weaken the effectiveness of risk assessment and lead to human-machine conflicts. By combining subjective cognition of drivers and objective risk metrics, a policy-oriented cognitive risk map (POCRM) is proposed in this paper to encode different driving policies in risk assessment for lane-changing scenarios. To obtain the objective safety metrics, insecurity quantification is built based on the fuzzy theory and fault tree analysis. The subjective cognition of drivers for different driving policies is modeled by deep successor representation and encoded in POCRM using deep reinforcement learning. Driving data collected from the public dataset for realistic traffic environment are used to evaluate the proposed POCRM. The experimental results show that the risk map can take into account future risks and provide driving advice that balances human-machine conflicts with safety in scenarios where drivers can or cannot correctly perceive risk.

Accurate assessment of the cyclist risk is a crucial task for the safety system of autonomous vehicles (AVs). This paper proposes a framework for defining and evaluating cyclist risk levels, considering behavioral cues. The framework comprises three modules: the cyclist graph construction (CGC) module, the risk label generation (RLG) module, and the risk assessment (RA) module. The CGC module constructs a spatiotemporal graph model of the cyclist with both the behavioral and risk information. The RLG module leverages the graph representation method (GRM) to extract features and assigns risk labels using unsupervised learning. The RA module employs spatiotemporal graph convolutional networks (ST-GCN) to extract features from the cyclist graph. Additionally, it facilitates feature fusion through interactions between the human body and the two-wheeler and between hierarchical levels. The fused features, along with the risk labels, are used to train a classifier for the risk assessment of cyclists. The proposed framework is validated using real-world data, and the comparative results with state-of-the-art methods demonstrate the effectiveness and accuracy of the proposed approach in cyclist risk assessment in mixed traffic.

As healthcare popularity increases, more people use food therapy for nourishment and healing. However, without scientific guidance, it's difficult to select appropriate foods for specific needs. To address the issue, we extract knowledge from TCMSP and professional books and fuse the data from different sources. Next, the Food Therapy Knowledge Graph (FTKG) is constructed. Finally, a food therapy system is developed that integrates the concept of TCMSP and FTKG, which uses the efficient knowledge retrieval and knowledge reasoning ability of the knowledge graph. It provides scientific food therapy solutions by analyzing symptoms and substituting traditional Chinese medicine with food, s address individual health needs.

The current network information of endangered plants is scattered, making acquiring and reusing relevant knowledge difficult. The endangered plants' knowledge graph ePlantKG constructed in this paper can form a visual semantic information network. The data sources in this paper include China Rare and Endangered Plant Information Network, Baidu Encyclopedia, Wikipedia, and Kuaiming Encyclopedia. Firstly, we use a Python crawler to obtain network data and preprocess them. Then we use the obtained structured data combined with previously constructed plant ontology to define and build new ontologies. Next, through the rule-based method, we extract triples from semi-structured and unstructured data, fuse knowledge between heterogeneous data, and store them in the Neo4j database to form ePlantKG. Finally, we design and build a knowledge service platform to illustrate knowledge graphs and intelligent question answering. The intelligent question answering algorithm extracts features from the user's input text with TF-IDF and classifies questions with Naive Bayes. After realizing the similarity matching between entities and relations, we retrieve answers with the returned Cypher statement. The ePlantKG records 1926 species of endangered plants and 37860 species of common plants, and the image filling rate of endangered plants is more than 99 %. The platform implements several functions, e.g., graph display, entity recognition, and intelligent question answering. This paper realizes the information sharing and reuses on endangered plants, providing a method reference for applying knowledge graph in forestry intelligent question answering system and forestry big data analysis.

At present, tree information on the Internet is not systematic enough. In order to solve this problem, we propose a tree knowledge graph namely TreeKG. After obtaining a mass of data, We construct TreeKG based on rules and deep learning and develop some application. This paper introduce the construction and application of TreeKG in detail, providing a new idea for the research on forestry knowledge graph.

Multi-hop Question Answering over Knowledge Graphs (KGQA) in previous studies has achieved remarkable results by exploiting the prediction property of Knowledge Graphs (KG) embedding. However, when facing Chinese sentences, its answer selection performs poorly. We improve the method for KGQA by combining the traditional method for KGQA with a lattice based CNN (LCN) model. We refine the granularity of questions and answers to make its coverage more extensive and generalizable, and expand the answer set to improve the performance in single results.