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Reachability-Based Confidence-Aware Probabilistic Collision Detection in Highway Driving

Engineering 2024年第2期33卷 90-107页

作者： Xinwei Wang Zirui Li Javier Alonso-Mora Meng Wang School of Engineering and Materials Science Queen Mary University of LondonLondon E14NSUK Department of Cognitive Robotics Delft University of TechnologyDelft 2628 CDNetherlands School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China Chair of Traffic Process Automation “Friedrich List”Faculty of Transport and Traffic SciencesTU DresdenDresden 01069Germany

Risk assessment is a crucial component of collision warning and avoidance systems for intelligent ***-based formal approaches have been developed to ensure driving safety to accurately detect potential vehicle ***,they suffer from over-conservatism,potentially resulting in false–positive risk events in complicated real-world *** this paper,we combine two reachability analysis techniques,a backward reachable set(BRS)and a stochastic forward reachable set(FRS),and propose an integrated probabilistic collision–detection framework for highway *** this framework,we can first use a BRS to formally check whether a two-vehicle interaction is safe;otherwise,a prediction-based stochastic FRS is employed to estimate the collision probability at each future time ***,the framework can not only identify non-risky events with guaranteed safety but also provide accurate collision risk estimation in safety-critical *** construct the stochastic FRS,we develop a neural network-based acceleration model for surrounding vehicles and further incorporate a confidence-aware dynamic belief to improve the prediction *** experiments were conducted to validate the performance of the acceleration prediction model based on naturalistic highway driving *** efficiency and effectiveness of the framework with infused confidence beliefs were tested in both naturalistic and simulated highway *** proposed risk assessment framework is promising for real-world applications.

关键词： Probabilistic collision detection Confidence awareness Probabilistic acceleration prediction Reachability analysis Risk assessment

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Two-Stage OD Flow Prediction for Emergency in Urban Rail Transit

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IEEE Transactions on Intelligent transportation Systems 2024年第1期25卷 920-928页

作者： Zhu, Guangyu Ding, Jiacun Wei, Yun Yi, Yang Xu, Sendren Sheng-Dong Wu, Edmond Q. Beijing Jiaotong University Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport The Beijing Research Center of Urban Traffic Information Sensing and Service Technologies Beijing100044 China Beijing Mass Transit Railway Operation Corporation Ltd. Beijing100014 China Yangzhou University College of Information Engineering Yangzhou225127 China National Taiwan University of Science and Technology Automation and Control Center The Graduate Institute of Automation and Control Taipei106335 Taiwan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China The Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai200240 China

Urban rail transit (URT) is vulnerable to natural disasters and social emergencies including fire, storm and epidemic (such as COVID-19), and real-time origin-destination (OD) flow prediction provides URT operators with important information to ensure the safety of URT system. However, hindered by the high dimensionality of OD flow and the lack of supportive information reflecting the real-time passenger flow changes, study in this area is at the beginning stage. A novel model consisting of two stages is proposed for OD flow prediction. The first stage predicts the inflows of all stations by Long Short-Term Memory (LSTM) in real time, where the dimension is reduced compared with predicting OD flows directly. In the second stage, the notion of separation rate, namely, the proportion of inbound passengers bounding for another station, is estimated. Finally, The OD flow is predicted by multiplying the inflow and separation rate. Experiments based on Hangzhou Metro dataset show the proposed model outperforms the contrast model in weighted mean average error (WMAE) and weighted mean square error (WMSE). Results also suggest that the proposed prediction model performs better on weekdays than on weekends, and with greater accuracy on larger OD flows. © 2000-2011 IEEE.

关键词： Forecasting

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Road life-cycle carbon dioxide emissions and emission reduction technologies:A review

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Journal of traffic and transportation Engineering(English Edition) 2022年第4期9卷 532-555页

作者： Nieyangzi Liu Yuanqing Wang Qiang Bai Yuanyuan Liu Peirong(Slade)Wang Shuqi Xue Qian Yu Qianrong Li Key Laboratory of Transport Industry of Management Control and Cycle Repair Technology for Traffic Network Facilities in Ecological Security Barrier AreaCollege of Transportation EngineeringChang'an UniversityXi'an 710064China School of Civil and Transportation Engineering Guangdong University of TechnologyGuangzhou 510006China Department of Civil and Environmental Engineering University of Texas at ArlingtonArlingtonTX 76019USA

Carbon dioxide(CO_(2))emissions from the road sector have attracted increasing attention in current *** paper attempted to provide a systematic review of the existing research efforts on road life-cycle CO_(2)emissions by analyzing the system’s boundary division,identifying the CO_(2)emission contributions of each life-cycle phase,listing major emission contributors,exploring related emission reduction technologies,and giving directions for future *** research showed that the road life cycle is usually divided into five phases:material production,construction,use,maintenance and end-of-life(EOL)*** use phase and the initial construction stage(including material production and construction phases)contributed the most CO_(2)emissions during the road life *** detail,the production of cement,asphalt and steel were the three main emission contributors in the material production *** pavement roughness,albedo,and concrete carbonation were the main factors affecting emissions in the use *** addition,emission reduction technologies such as using recycled materials and recycling techniques,lowering mixing temperature,and equipment energy substitution were commonly used to reduce emissions from material production and construction *** application of emerging technologies such as carbon capture and storage,carbon sink,and the use of hydrogen,solar and photovoltaic in the road sector may have emission reduction potentials and should be highlighted more in future studies.

关键词： Roads Life-cycle assessment Carbon dioxide emissions Emission reduction technologies

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Metarhizium spp. encode an ochratoxin cluster and a high efficiency ochratoxin-degrading amidohydrolase revealed by genomic analysis

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Journal of Advanced Research 2024年 72卷 85-95页

作者： Wang, Gang Wu, Wenqing Keller, Nancy P. Guo, Xu Li, Erfeng Ma, Junning Xing, Fuguo Key Laboratory of Agro-Products Quality and Safety Control in Storage and Transport Process Ministry of Agriculture and Rural Affairs Institute of Food Science and Technology Chinese Academy of Agricultural Sciences Beijing 100193 China Horticulture and Landscape College Tianjin Agricultural University Tianjin 300392 China Department of Medical Microbiology and Immunology University of Wisconsin-Madison Madison 53706 WI United States

Introduction: Ochratoxins (OTs) are worldwide regulated mycotoxins contaminating a variety of food-environment and agro-environment. Several Aspergillus and Pencillium species synthesize OTs from a six-gene biosynthetic gene cluster (BGC) to produce the highly toxic final product OTA. Although many studies on OTA-degrading enzymes were performed, high efficiency enzymes with strong stability are extremely needed, and the OTA degrading mechanism is poorly understood. Objectives: The study aimed to explore the OT-degradation enzyme and investigate its degradation mechanisms in Metarhizium, which contain an OT biosynthetic gene cluster. Methods: Phylogenomic relationship combined with RNA expression analysis were used to explore the distribution of OT BGC in fungi. Bioactivity-guided isolation and protein mass spectrometry were conducted to trace the degrading enzymes in Metarhizium spp., and the enzymes were heterologously expressed in E. coli and verified by in vitro assays. Structure prediction and point mutation were performed to reveal the catalytic mechanism of MbAmh1. Results: Beyond Aspergillus and Pencillium species, three species of the distant phylogenetic taxon Metarhizium contain an expressed OT-like BGC but lack an otaD gene. Unexpectedly, no OT BGC products were found in some Metarhizium species. Instead, Metarhizium metabolized both OTA and OTB to their non-toxic degradation products. This activity of M. brunneum was attributed to an intracellular hydrolase MbAmh1, which was tracked by bioactivity-guided proteomic analysis combined with in vitro reaction. Recombinant MbAmh1 (5 μg/mL) completely degraded 1 μg/mL OTA within 3 min, demonstrating a strong degrading ability towards OTA. Additionally, MbAmh1 showed considerable temperature adaptability ranging from 30 to 70 °C and acidic pH stability ranging from 4.0 to 7.0. Identification of active sites supported the crucial role of metal iron for this enzymatic reaction. Conclusion: These findings reveal d

关键词： Detoxification Mycotoxin Ochratoxigenic fungi OTA Secondary metabolite

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Research study of image classification algorithms based on Convolutional Neural Networks

Research study of image classification algorithms based on C...

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International Carpathian control Conference (ICCC)

作者： Agata Nawrocka Marcin Nawrocki Andrzej Kot Department of Process Control Faculty of Mechanical Engineering and Robotics AGH University of Science and Technology Krakow Poland Department of Mechinery Engineering and Transport Faculty of Mechanical Engineering and Robotics AGH University of Science and Technology Krakow Poland

The beginning of the paper introduces the idea of neural networks by presenting the neuron models and artificial neural networks. Next, the flow of operations used in the development of convolutional neural networks is presented, as well as an overview of popular architectures used in model development. After introducing the theoretical issues, a project using the EfficientNet architecture to recognize medical images taken by MRI is presented. The project describes: the selection and parameters of the MRI image database representing different types of brain tumors, the process of implementation and appropriate modification of the EfficientNet architecture used to classify images, the presentation of the results of learning the convolutional neural network and the images that highlight on the basis of which area the convolutional neural network classifies images.

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Leveraging Multi-stream Information Fusion for Trajectory Prediction in Low-illumination Scenarios: A Multi-channel Graph Convolutional Approach

arXiv

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arXiv 2022年

作者： Gong, Hailong Li, Zirui Lu, Chao Du, Guodong Gong, Jianwei School of Mechanical Engineering Beijing Institute of Technology Beijing100081 China Chair of Traffic Process Automation "Friedrich List" Faculty of Transport and Traffic Sciences TU Dresden Germany Institute of Dynamic System and Control ETH Zurich Sonneggstrasse 3 Zurich8092 Switzerland

Trajectory prediction is a fundamental problem and challenge for autonomous vehicles. Early works mainly focused on designing complicated architectures for deep-learning-based prediction models in normal-illumination environments, which fail in dealing with low-light conditions. This paper proposes a novel approach for trajectory prediction in low-illumination scenarios by leveraging multi-stream information fusion, which flexibly integrates image, optical flow, and object trajectory information. The image channel employs Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM) networks to extract temporal information from the camera. The optical flow channel is applied to capture the pattern of relative motion between adjacent camera frames and modelled by Spatial-Temporal Graph Convolutional Network (ST-GCN). The trajectory channel is used to recognize high-level interactions between vehicles. Finally, information from all the three channels is effectively fused in the prediction module to generate future trajectories of surrounding vehicles in low-illumination conditions. The proposed multi-channel graph convolutional approach is validated on HEV-I and newly generated Dark-HEV-I, egocentric vision datasets that primarily focus on urban intersection scenarios. The results demonstrate that our method outperforms the baselines, in standard and low-illumination scenarios. Additionally, our approach is generic and applicable to scenarios with different types of perception data. The source code of the proposed approach is available at https://***/TommyGong08/MSIF. Copyright © 2022, The Authors. All rights reserved.

关键词： Optical flows

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Reachability-Based Confidence-Aware Probabilistic Collision Detection in Highway Driving

arXiv

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arXiv 2023年

作者： Wang, Xinwei Li, Zirui Alonso-Mora, Javier Wang, Meng School of Engineering and Materials Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Cognitive Robotics Delft University of Technology Delft2628 CD Netherlands School of Mechanical Engineering Beijing Institute of Technology Beijing100081 China Traffic Process Automation "Friedrich List" Faculty of Transport and Traffic Sciences TU Dresden Dresden01069 Germany

Risk assessment is a crucial component of collision warning and avoidance systems in intelligent vehicles. To accurately detect potential vehicle collisions, reachability-based formal approaches have been developed to ensure driving safety, but suffer from over-conservatism, potentially leading to false-positive risk events in complicated real-world applications. In this work, we combine two reachability analysis techniques, i.e., backward reachable set (BRS) and stochastic forward reachable set (FRS), and propose an integrated probabilistic collision detection framework in highway driving. Within the framework, we can firstly use a BRS to formally check whether a two-vehicle interaction is safe;otherwise, a prediction-based stochastic FRS is employed to estimate a collision probability at each future time step. In doing so, the framework can not only identify non-risky events with guaranteed safety, but also provide accurate collision risk estimation in safety-critical events. To construct the stochastic FRS, we develop a neural network-based acceleration model for surrounding vehicles, and further incorporate confidence-aware dynamic belief to improve the prediction accuracy. Extensive experiments are conducted to validate the performance of the acceleration prediction model based on naturalistic highway driving data, and the efficiency and effectiveness of the framework with the infused confidence belief are tested both in naturalistic and simulated highway scenarios. The proposed risk assessment framework is promising in real-world applications. © 2023, CC BY.

关键词： Forecasting

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Graph Reinforcement Learning Application to Co-operative Decision-Making in Mixed Autonomy traffic: Framework, Survey, and Challenges

arXiv

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arXiv 2022年

作者： Liu, Qi Li, Xueyuan Li, Zirui Wu, Jingda Du, Guodong Gao, Xin Yang, Fan Yuan, Shihua School of Mechanical Engineering Beijing Institute of Technology Beijing China Chair of Traffic Process Automation "Friedrich List" Faculty of Transport and Traffic Sciences TU Dresden Germany School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore639798 Singapore Institute of Dynamic System and Control ETH Zurich Zurich Switzerland

Proper functioning of connected and automated vehicles (CAVs) is crucial for the safety and efficiency of future intelligent transport systems. Meanwhile, transitioning to fully autonomous driving requires a long period of mixed autonomy traffic, including both CAVs and human-driven vehicles. Thus, collaboration decision-making for CAVs is essential to generate appropriate driving behaviors to enhance the safety and efficiency of mixed autonomy traffic. In recent years, deep reinforcement learning (DRL) has been widely used in solving decision-making problems. However, the existing DRL-based methods have been mainly focused on solving the decision-making of a single CAV. Using the existing DRL-based methods in mixed autonomy traffic cannot accurately represent the mutual effects of vehicles and model dynamic traffic environments. To address these shortcomings, this article proposes a graph reinforcement learning (GRL) approach for multi-agent decision-making of CAVs in mixed autonomy traffic. First, a generic and modular GRL framework is designed. Then, a systematic review of DRL and GRL methods is presented, focusing on the problems addressed in recent research. Moreover, a comparative study on different GRL methods is further proposed based on the designed framework to verify the effectiveness of GRL methods. Results show that the GRL methods can well optimize the performance of multi-agent decision-making for CAVs in mixed autonomy traffic compared to the DRL methods. Finally, challenges and future research directions are summarized. This study can provide a valuable research reference for solving the multi-agent decision-making problems of CAVs in mixed autonomy traffic and can promote the implementation of GRL-based methods into intelligent transportation systems. The source code of our work can be found at https://***/Jacklinkk/Graph_CAVs. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Genomic and metabolomic insights into the selection and differentiation of bioactive compounds in citrus

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Molecular Plant 2024年第11期17卷 1753-1772页

作者： Xiao Liang Yue Wang Wanxia Shen Bin Liao Xiaojuan Liu Zimeng Yang Jiebiao Chen Chenning Zhao Zhenkun Liao Jinping Cao Ping Wang Peng Wang Fuzhi Ke Jianguo Xu Qiong Lin Wanpeng Xi Lishu Wang Juan Xu Xiaochun Zhao Chongde Sun Plant Growth Development and Quality ImprovementZhejiang Provincial Key Laboratory of Integrative Biology of Horticultural PlantsZhejiang UniversityHangzhouChina Citrus Research Institute Southwest University/Chinese Academy of Agricultural SciencesChongqingChina Hainan Institute of Zhejiang University SanyaChina Zhejiang Citrus Research Institute TaizhouChina Key Laboratory of Agro-products Quality and Safety Control in Storage and Transport Process Ministry of Agriculture and Rural Affairs/Institute of Food Science and TechnologyChinese Academy of Agricultural SciencesBeijingChina College of Horticulture and Landscape Architecture Southwest UniversityChongqingChina Department of Hematology and Hematopoietic Cell Transplantation Comprehensive Cancer CenterCity of Hope National Medical CenterDuarteCAUSA Key Laboratory of Horticultural Plant Biology(Ministry of Education) Huazhong Agricultural UniversityWuhanChina

Bioactive compounds play an increasingly prominent role in breeding functional and nutritive fruit crops such as ***,the genomic and metabolic bases for the selection and differentiation underlying bioactive compound variations in citrus remain poorly *** this study,we constructed a species-level variation atlas of genomes and metabolomes using 299 citrus accessions.A total of 19829 significant SNPs were targeted to 653 annotated metabolites,among which multiple significant signals were identified for secondary metabolites,especially *** differential accumulation of bioactive compounds in the phenylpropane pathway,mainly flavonoids and coumarins,was unveiled across ancestral citrus species during differentiation,which is likely associated with the divergent haplotype distribution and/or expression profiles of relevant genes,including p-coumaroyl coenzyme A 2′-hydroxylases,flavone synthases,cytochrome P450 enzymes,prenyltransferases,and uridine diphosphate ***,we systematically evaluated the beneficial bioactivities such as the antioxidant and anticancer capacities of 219 citrus varieties,and identified robust associations between distinct bioactivities and specific ***,these findings provide citrus breeding options for enrichment of beneficial flavonoids and avoidance of potential risk of *** study will accelerate the application of genomic and metabolic engineering strategies in developing modern healthy citrus cultivars.

关键词： citrus metabolome genome-wide association study bioactive compounds

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Prediction-Based Reachability Analysis for Collision Risk Assessment on Highways

arXiv

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arXiv 2022年

作者： Wang, Xinwei Li, Zirui Alonso-Mora, Javier Wang, Meng Department of Transport and Planning Delft University of Technology Delft2628 CD Netherlands School of Mechanical Engineering Beijing Institute of Technology Beijing100081 China Department of Cognitive Robotics Delft University of Technology Delft2628 CD Netherlands Traffic Process Automation Technische Universität Dresden Dresden Germany

Real-time safety systems are crucial components of intelligent vehicles. This paper introduces a prediction-based collision risk assessment approach on highways. Given a point mass vehicle dynamics system, a stochastic forward reachable set considering two-dimensional motion with vehicle state probability distributions is firstly established. We then develop an acceleration prediction model, which provides multi-modal probabilistic acceleration distributions to propagate vehicle states. The collision probability is calculated by summing up the probabilities of the states where two vehicles spatially overlap. Simulation results show that the prediction model has superior performance in terms of vehicle motion position errors, and the proposed collision detection approach is agile and effective to identify the collision in cut-in crash events. © 2022, CC BY.

关键词： Probability distributions

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