A Driving Risk Surrogate and Its Application in Car-Following Scenario at Expressway

Traffic safety is important in reducing death and building a harmonious society. In addition to studies of accident incidences, the perception of driving risk is significant in guiding the implementation of appropriate driving countermeasures. Risk assessment can be conducted in real-time for traffic safety due to the rapid development of communication technology and computing capabilities. This paper aims at the problems of difficult calibration and inconsistent thresholds in the existing risk assessment methods. It proposes a risk assessment model based on the potential field to quantify the driving risk of vehicles. Firstly, virtual energy is proposed as an attribute considering vehicle sizes and velocity. Secondly, the driving risk surrogate(DRS) is proposed based on potential field theory to describe the risk degree of vehicles. Risk factors are quantified by establishing submodels, including an interactive vehicle risk surrogate, a restrictions risk surrogate, and a speed risk surrogate. To unify the risk threshold, acceleration for implementation guidance is derived from the risk field strength. Finally, a naturalistic driving dataset in Nanjing, China, is selected, and 3063 pairs of following naturalistic trajectories are screened out. Based on that, the proposed model and other models use for comparisons are calibrated through the improved particle optimization algorithm. Simulations prove that the proposed model performs better than other algorithms in risk perception and response, car-following trajectory, and velocity estimation. In addition, the proposed model exhibits better car-following ability than existing car-following models

Adaptive Assignment for Geometry Aware Local Feature Matching

The detector-free feature matching approaches are currently attracting great attention thanks to their excellent performance. However, these methods still struggle at large-scale and viewpoint variations, due to the geometric inconsistency resulting from the application of the mutual nearest neighbour criterion (\ie, one-to-one assignment) in patch-level matching.Accordingly, we introduce AdaMatcher, which first accomplishes the feature correlation and co-visible area estimation through an elaborate feature interaction module, then performs adaptive assignment on patch-level matching while estimating the scales between images, and finally refines the co-visible matches through scale alignment and sub-pixel regression module.Extensive experiments show that AdaMatcher outperforms solid baselines and achieves state-of-the-art results on many downstream tasks. Additionally, the adaptive assignment and sub-pixel refinement module can be used as a refinement network for other matching methods, such as SuperGlue, to boost their performance further. The code will be publicly available at https://github.com/AbyssGaze/AdaMatcher.Comment: Accepted by CVPR202

OccTransformer: Improving BEVFormer for 3D camera-only occupancy prediction

This technical report presents our solution, "occTransformer" for the 3D occupancy prediction track in the autonomous driving challenge at CVPR 2023. Our method builds upon the strong baseline BEVFormer and improves its performance through several simple yet effective techniques. Firstly, we employed data augmentation to increase the diversity of the training data and improve the model's generalization ability. Secondly, we used a strong image backbone to extract more informative features from the input data. Thirdly, we incorporated a 3D unet head to better capture the spatial information of the scene. Fourthly, we added more loss functions to better optimize the model. Additionally, we used an ensemble approach with the occ model BevDet and SurroundOcc to further improve the performance. Most importantly, we integrated 3D detection model StreamPETR to enhance the model's ability to detect objects in the scene. Using these methods, our solution achieved 49.23 miou on the 3D occupancy prediction track in the autonomous driving challenge.Comment: Innovation Award in the 3D Occupancy Prediction Challenge (CVPR23

LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion

LiDAR-camera fusion methods have shown impressive performance in 3D object detection. Recent advanced multi-modal methods mainly perform global fusion, where image features and point cloud features are fused across the whole scene. Such practice lacks fine-grained region-level information, yielding suboptimal fusion performance. In this paper, we present the novel Local-to-Global fusion network (LoGoNet), which performs LiDAR-camera fusion at both local and global levels. Concretely, the Global Fusion (GoF) of LoGoNet is built upon previous literature, while we exclusively use point centroids to more precisely represent the position of voxel features, thus achieving better cross-modal alignment. As to the Local Fusion (LoF), we first divide each proposal into uniform grids and then project these grid centers to the images. The image features around the projected grid points are sampled to be fused with position-decorated point cloud features, maximally utilizing the rich contextual information around the proposals. The Feature Dynamic Aggregation (FDA) module is further proposed to achieve information interaction between these locally and globally fused features, thus producing more informative multi-modal features. Extensive experiments on both Waymo Open Dataset (WOD) and KITTI datasets show that LoGoNet outperforms all state-of-the-art 3D detection methods. Notably, LoGoNet ranks 1st on Waymo 3D object detection leaderboard and obtains 81.02 mAPH (L2) detection performance. It is noteworthy that, for the first time, the detection performance on three classes surpasses 80 APH (L2) simultaneously. Code will be available at \url{https://github.com/sankin97/LoGoNet}.Comment: Accepted by CVPR202

Non-invasive brain stimulation for treatment-resistant schizophrenia: protocol of a systematic review and network meta-analysis.

BACKGROUND Non-invasive brain stimulation (NIBS) is a promising intervention for treatment-resistant schizophrenia. However, there are multiple available techniques and a comprehensive synthesis of evidence is lacking. Thus, we will conduct a systematic review and network meta-analysis to investigate the comparative efficacy and safety of NIBS techniques as an add-on to antipsychotics for treatment-resistant schizophrenia. METHODS We will include single- and double-blind randomized-controlled trials (RCT) comparing any NIBS technique with each other or with a control intervention as an add-on to antipsychotics in adult patients with treatment-resistant schizophrenia. We will exclude studies focusing on predominant negative symptoms, maintenance treatment, and single sessions. The primary outcome will be a change in overall symptoms, and secondary outcomes will be a change in symptom domains, cognitive performance, quality of life, functioning, response, dropouts, and side effects. We will search for eligible studies in previous reviews, multiple electronic databases and clinical trial registries from inception onwards. At least two independent reviewers will perform the study selection, data extraction, and risk of bias assessment. We will measure the treatment differences using standardized mean difference (SMD) and odds ratio (OR) for continuous and dichotomous outcomes, respectively. We will conduct pairwise and network meta-analysis within a frequentist framework using a random-effects model, except for rare event outcomes where we will use a fixed-effects Mantel-Haenszel method. We will investigate potential sources of heterogeneity in subgroup analyses. Reporting bias will be assessed with funnel plots and the Risk of Bias due to Missing Evidence in Network meta-analysis (ROB-MEN) tool. The certainty in the evidence will be evaluated using the Confidence in Network Meta-analysis (CINeMA) approach. DISCUSSION Our network meta-analysis would provide an up-to-date synthesis of the evidence from all available RCTs on the comparative efficacy and safety of NIBS for treatment-resistant schizophrenia. This information could guide evidence-based clinical practice and improve the outcomes of patients. SYSTEMATIC REVIEW REGISTRATION PROSPERO-ID CRD42023410645

Exercise for frailty research frontiers: a bibliometric analysis and systematic review

BackgroundExercise intervention is a method of improving and preventing frailty in old age through physical exercise and physical activity. It has a positive impact on many chronic diseases and health risk factors, in particular cardiovascular disease, metabolic disease, osteoporosis, mental health problems and cancer prevention, and exercise therapies can also fight inflammation, increase muscle strength and flexibility, improve immune function, and enhance overall health. This study was aimed to analyze research hotspots and frontiers in exercise therapies for frailty through bibliometric methods.MethodsIn this study, data of publications from 1st January 2003 to 31st August 2023 were gathered from the Web of Science Core Collection and analyzed the hotspots and frontiers of frailty research in terms of remarkable countries/regions, institutions, cited references, authors, cited journals, burst keywords, and high-frequency keywords using CiteSpace 6.2.R3 software. The PRISMA reporting guidelines were used for this study.ResultsA collection of 7,093 publications was obtained, showing an increasing trend each year. BMC Geriatrics led in publications, while Journals of Gerontology Series A-Biological Sciences and Medical Sciences dominated in citations. The United States led in centrality and publications, with the University of Pittsburgh as the most productive institution. Leocadio R had the highest publication ranking, while Fried Lp ranked first among cited authors. Keywords in the domain of exercise therapies for frailty are “frailty,” “older adult,” “physical activity,” “exercise,” and “mortality,” with “sarcopenia” exhibiting the greatest centrality. The keywords formed 19 clusters, namely “#0 older persons,” “#1 mortality,” “#2 muscle strength,” “#3 bone mineral density,” “#4 muscle mass,” “#5 older adults,” “#6 older people,” “#7 women’s health,” “#8 frail elderly,” “#9 heart failure,” “#10 geriatric assessment,” “#11 comprehensive geriatric assessment,” “#12 outcm,” “#13 alzheimers disease,” “#14 quality of life,” “#15 health care,” “#16 oxidative stress,” “#17 physical activity,” and “#18 protein.”ConclusionThis study presents the latest developments and trends in research on frailty exercise intervention treatments over the past 20 years using CiteSpace visualization software. Through systematic analyses, partners, research hotspots and cutting-edge directions were revealed, providing a guiding basis for future research