Driver anomaly quantification for intelligent vehicles: a contrastive learning approach with representation clustering

Driver anomaly quantification is a fundamental capability to support human-centric driving systems of intelligent vehicles. Existing studies usually treat it as a classification task and obtain discrete levels for abnormalities. Meanwhile, the existing data-driven approaches depend on the quality of dataset and provide limited recognition capability for unknown activities. To overcome these challenges, this paper proposes a contrastive learning approach with the aim of building a model that can quantify driver anomalies with a continuous variable. In addition, a novel clustering supervised contrastive loss is proposed to optimize the distribution of the extracted representation vectors to improve the model performance. Compared with the typical contrastive loss, the proposed loss can better cluster normal representations while separating abnormal ones. The abnormality of driver activity can be quantified by calculating the distance to a set of representations of normal activities rather than being produced as the direct output of the model. The experiment results with datasets under different modes demonstrate that the proposed approach is more accurate and robust than existing ones in terms of recognition and quantification of unknown abnormal activities

Toward human-centered automated driving: a novel spatial-temporal vision transformer-enabled head tracker

Accurate dynamic driver head pose tracking is of great importance for driver–automotive collaboration, intelligent copilot, head-up display (HUD), and other human-centered automated driving applications. To further advance this technology, this article proposes a low-cost and markerless headtracking system using a deep learning-based dynamic head pose estimation model. The proposed system requires only a red, green, blue (RGB) camera without other hardware or markers. To enhance the accuracy of the driver’s head pose estimation, a spatiotemporal vision transformer (ST-ViT) model, which takes an image pair as the input instead of a single frame, is proposed. Compared to a standard transformer, the ST-ViT contains a spatial–convolutional vision transformer and a temporal transformer, which can improve the model performance. To handle the error fluctuation of the head pose estimation model, this article proposes an adaptive Kalman filter (AKF). By analyzing the error distribution of the estimation model and the user experience of the head tracker, the proposed AKF includes an adaptive observation noise coefficient; this can adaptively moderate the smoothness of the curve. Comprehensive experiments show that the proposed system is feasible and effective, and it achieves a state-of-the-art performance.Agency for Science, Technology and Research (A*STAR)Nanyang Technological UniversityThis work was supported in part by in part by the A*STAR National Robotics Program under grant W1925d0046, the Start-Up Grant, Nanyang Assistant Professorship under grant M4082268.050, Nanyang Technological University, Singapore, and the State Key Laboratory of Automotive Safety and Energy under project KF2021

Review and perspectives on driver digital twin and its enabling technologies for intelligent vehicles

Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the design of the automated vehicle, whereas the digitization of the human driver, who plays an important role in driving, is largely ignored. Furthermore, previous driver-related tasks are limited to specific scenarios and have limited applicability. Thus, a novel concept of a driver digital twin (DDT) is proposed in this study to bridge the gap between existing automated driving systems and fully digitized ones and aid in the development of a complete driving human cyber-physical system (H-CPS). This concept is essential for constructing a harmonious human-centric intelligent driving system that considers the proactivity and sensitivity of the human driver. The primary characteristics of the DDT include multimodal state fusion, personalized modeling, and time variance. Compared with the original DT, the proposed DDT emphasizes on internal personality and capability with respect to the external physiological-level state. This study systematically illustrates the DDT and outlines its key enabling aspects. The related technologies are comprehensively reviewed and discussed with a view to improving them by leveraging the DDT. In addition, the potential applications and unsettled challenges are considered. This study aims to provide fundamental theoretical support to researchers in determining the future scope of the DDT system

Study on Therapeutic Action and Mechanism of TMZ Combined with RITA Against Glioblastoma

Background/Aims: Glioblastoma multiforme (GBM) is a malignant and aggressive central nervous system (CNS) tumor with high mortality and low survival rate. Effective treatment of GMB is a challenge worldwide. Temozolomide (TMZ) is a drug used to treat GBM, while the survival period of GBM patients with positive treatment remains less than 15 months. Reactivating p53 and Inducing Tumor Apoptosis (RITA) is a novel potential anti-cancer small molecular drug. Thus, it is essential to discover novel targets or develop effective drugs combination strategy to treat GBM. Methods: The U87 cells and U251 cells (p53 mutated) were treated with DMSO and 1, 5,10, 20 μM RITA, TMZ, RITA+TMA or PFT-α. The cell proliferation was measured using the MTS cell proliferation assay. The cell apoptosis was analyzed by Annexin V-FITC/PI Apoptosis Detection Kit. The key protein expression level was evaluated by WB. Molecular docking and molecular dynamics (MD) simulation methods were applied to simulate the interaction between RITA and ASK1. Results: Herein, we found that combination RITA and TMZ effectively inhibited the proliferation of U87 cells and promoted the apoptosis of U87 cells. Then the mechanism of RITA and TMZ treating GBM were further studied by detecting the expression of the proteins associating with p53 pathway, such as ASK1, Bax, and so on. RITA bound to the amino acids residues in the activation domain of the ASK1, then induced the conformation change of ASK1 receptor, activated ASK1 and caused a series of signal transduction, further resulted in the physiological effects. Conclusion: Taken together, the RITA suppressed the cell proliferation in glioblastoma via targeting ASK1

Milestones in Autonomous Driving and Intelligent Vehicles Part \uppercase\expandafter{\romannumeral1}: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors

Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits. Although a number of surveys have reviewed research achievements in this field, they are still limited in specific tasks and lack systematic summaries and research directions in the future. Our work is divided into 3 independent articles and the first part is a Survey of Surveys (SoS) for total technologies of AD and IVs that involves the history, summarizes the milestones, and provides the perspectives, ethics, and future research directions. This is the second part (Part \uppercase\expandafter{\romannumeral1} for this technical survey) to review the development of control, computing system design, communication, High Definition map (HD map), testing, and human behaviors in IVs. In addition, the third part (Part \uppercase\expandafter{\romannumeral2} for this technical survey) is to review the perception and planning sections. The objective of this paper is to involve all the sections of AD, summarize the latest technical milestones, and guide abecedarians to quickly understand the development of AD and IVs. Combining the SoS and Part \uppercase\expandafter{\romannumeral2}, we anticipate that this work will bring novel and diverse insights to researchers and abecedarians, and serve as a bridge between past and future.Comment: 18 pages, 4 figures, 3 table

Milestones in autonomous driving and intelligent vehicles: survey of surveys

Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits. Although a number of surveys have reviewed research achievements in this field, they are still limited in specific tasks, lack of systematic summary and research directions in the future. Here we propose a Survey of Surveys (SoS) for total technologies of AD and IVs that reviews the history, summarizes the milestones, and provides the perspectives, ethics, and future research directions. To our knowledge, this article is the first SoS with milestones in AD and IVs, which constitutes our complete research work together with two other technical surveys. We anticipate that this article will bring novel and diverse insights to researchers and abecedarians, and serve as a bridge between past and future

The computational implementation of a platform of relative identity-by-descent scores algorithm for introgressive mapping

With the development of genotyping and sequencing technology, researchers working in the area of conservation genetics are able to obtain the genotypes or even the sequences of a representative sample of individuals from the population. It is of great importance to examine the genomic variants and genes that are highly preferred or pruned during the process of adaptive introgression or long-term hybridization. To the best of our knowledge, we are the first to develop a platform with computational integration of a relative identity-by-descent (rIBD) scores algorithm for introgressive mapping. The rIBD algorithm is designed for mapping the fine-scaled genomic regions under adaptive introgression between the source breeds and the admixed breed. Our rIBD calculation platform provides compact functions including reading input information and uploading of files, rIBD calculation, and presentation of the rIBD scores. We analyzed the simulated data using the rIBD calculation platform and calculated the average IBD score of 0.061 with a standard deviation of 0.124. The rIBD scores generally follow a normal distribution, and a cut-off of 0.432 and −0.310 for both positive and negative rIBD scores is derived to enable the identification of genomic regions showing significant introgression signals from the source breed to the admixed breed. A list of genomic regions with detailed calculated rIBD scores is reported, and all the rIBD scores for each of the considered windows are presented in plots on the rIBD calculation platform. Our rIBD calculation platform provides a user-friendly tool for the calculation of fine-scaled rIBD scores for each of the genomic regions to map possible functional genomic variants due to adaptive introgression or long-term hybridization

The computational implementation of a platform of relative identity-by-descent scores algorithm for introgressive mapping

With the development of genotyping and sequencing technology, researchers working in the area of conservation genetics are able to obtain the genotypes or even the sequences of a representative sample of individuals from the population. It is of great importance to examine the genomic variants and genes that are highly preferred or pruned during the process of adaptive introgression or long-term hybridization. To the best of our knowledge, we are the first to develop a platform with computational integration of a relative identity-by-descent (rIBD) scores algorithm for introgressive mapping. The rIBD algorithm is designed for mapping the fine-scaled genomic regions under adaptive introgression between the source breeds and the admixed breed. Our rIBD calculation platform provides compact functions including reading input information and uploading of files, rIBD calculation, and presentation of the rIBD scores. We analyzed the simulated data using the rIBD calculation platform and calculated the average IBD score of 0.061 with a standard deviation of 0.124. The rIBD scores generally follow a normal distribution, and a cut-off of 0.432 and −0.310 for both positive and negative rIBD scores is derived to enable the identification of genomic regions showing significant introgression signals from the source breed to the admixed breed. A list of genomic regions with detailed calculated rIBD scores is reported, and all the rIBD scores for each of the considered windows are presented in plots on the rIBD calculation platform. Our rIBD calculation platform provides a user-friendly tool for the calculation of fine-scaled rIBD scores for each of the genomic regions to map possible functional genomic variants due to adaptive introgression or long-term hybridization

Single-Atom Fe Nanozyme with Enhanced Oxidase-like Activity for the Colorimetric Detection of Ascorbic Acid and Glutathione

Single-atom nanozymes (SAzymes) have drawn ever-increasing attention due to their maximum atom utilization efficiency and enhanced enzyme-like activity. Herein, a facile pyrolysis strategy is reported for the synthesis of the iron–nitrogen–carbon (Fe-N-C) SAzyme using ferrocene trapped within porous zeolitic imidazolate framework-8 (ZIF-8@Fc) as a precursor. The as-prepared Fe-N-C SAzyme exhibited exceptional oxidase-mimicking activity, catalytically oxidizing 3,3′,5,5′-tetramethylbenzidine (TMB) with high affinity (Km) and fast reaction rate (Vmax). Taking advantage of this property, we designed two colorimetric sensing assays based on different interaction modes between small molecules and Fe active sites. Firstly, utilizing the reduction activity of ascorbic acid (AA) toward oxidized TMB (TMBox), a colorimetric bioassay for AA detection was established, which exhibited a good linear range of detection from 0.1 to 2 μM and a detection limit as low as 0.1 μM. Additionally, based on the inhibition of nanozyme activity by the thiols of glutathione (GSH), a colorimetric biosensor for GSH detection was constructed, showing a linear response over a concentration range of 1–10 μM, with a detection limit of 1.3 μM. This work provides a promising strategy for rationally designing oxidase-like SAzymes and broadening their application in biosensing