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检索条件"任意字段=25th International Conference on Intelligent Data Engineering and Automated Learning"

共 3588 条记录，以下是181-190 订阅

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A Deep Reinforcement learning Approach for automated OnRamp Merging 25

A Deep Reinforcement Learning Approach for Automated OnRamp ...

引用

IEEE 25th international conference on intelligent Transportation Systems (ITSC)

作者： Zhao, Ruibin Sun, Zhanbo Ji, Ang Southwest Jiaotong Univ Sch Transportat & Logist Natl Engn Lab Integrated Transportat Big Data App Chengdu Peoples R China

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

Safe and efficient control for autonomous vehicles (AVs) at on-ramp merging sections is challenging. Previous work about on-ramp merging focuses on discrete behavioral decisions and longitudinal motion control, while the lanechanging (LC) behavior is usually oversimplified. To bridge the research gaps, an on-ramp merging optimization control framework (ORMOC) based on a deep reinforcement learning (DRL) approach is proposed in the paper, which jointly optimizes the control of lane-keeping (LK) and LC. In the framework, firstly, a LC agent based on DRL and quintic polynomial-based trajectory model is trained to generate a smooth LC trajectory. then, a LK agent combined with the LC agent is trained by sharing learning, for longitudinal motion control LC decision execution. Finally, a priority-based safety supervisor is developed to enhance the safety of the control output. the proposed method is examined using SUMO simulation experiments, and the results demonstrate that compared to the TTC (time to collision)-based method, the proposed DRL-ORMOC framework optimizes the travel time by 8.3%.

关键词： on-ramp merging lane-keeping (LK) lane-changing (LC) deep reinforcement learning (DRL)

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Advanced Research in Applied Artificial Intelligence 2012

引用

丛书名： Lecture Notes in Computer Science

2012年

作者： He Jiang Wei Ding Moonis Ali Xindong Wu

ISBN: (数字)9783642310874

ISBN: (纸本)9783642310867

this volume constitutes the thoroughly refereed conference proceedings of the 25th international conference on Industrial engineering and Other Applications of Applied Intelligend Systems, IEA/AIE 2012, held in Dalian, China, in June 2012. the total of 82 papers selected for the proceedings were carefully reviewed and selected from numerous submissions. the papers are organized in topical sections on machine learning methods; cyber-physical system for intelligent transportation applications; AI applications; evolutionary algorithms, combinatorial optimization; modeling and support of cognitive and affective human processes; natural language processing and its applications; social network and its applications; mission-critical applications and case studies of intelligent systems; AI methods; sentiment analysis for asian languages; aspects on cognitive computing and intelligent interaction; spatio-temporal datamining, structured learning and their applications; decision making and knowledge based systems; pattern recognition; agent based systems; decision making techniques and innovative knowledge management; machine learning applications.

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Prediction Based Decision Making for Autonomous Highway Driving 25

Prediction Based Decision Making for Autonomous Highway Driv...

引用

IEEE 25th international conference on intelligent Transportation Systems (ITSC)

作者： Yildirim, Mustafa Mozaffari, Sajjad McCutcheon, Luc Dianati, Mehrdad Tamaddoni-Nezhad, Alireza Fallah, Saber Univ Surrey Dept Mech Engn Sci CAV Lab Guildford Surrey England Univ Surrey Dept Comp Sci Guildford Surrey England Univ Warwick Warwick Mfg Grp Warwick England

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

Autonomous driving decision-making is a challenging task due to the inherent complexity and uncertainty in traffic. For example, adjacent vehicles may change their lane or overtake at any time to pass a slow vehicle or to help traffic flow. Anticipating the intention of surrounding vehicles, estimating their future states and integrating them into the decision-making process of an automated vehicle can enhance the reliability of autonomous driving in complex driving scenarios. this paper proposes a Prediction-based Deep Reinforcement learning (PDRL) decision-making model that considers the manoeuvre intentions of surrounding vehicles in the decision-making process for highway driving. the model is trained using real traffic data and tested in various traffic conditions through a simulation platform. the results show that the proposed PDRL model improves the decision-making performance compared to a Deep Reinforcement learning (DRL) model by decreasing collision numbers, resulting in safer driving.

关键词： Road transportation Uncertainty Decision making Reinforcement learning Predictive models data models Safety

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MD-STCN: A deep learning-based architecture considering multivariate disturbances for metro passenger flow prediction 25

MD-STCN: A deep learning-based architecture considering mult...

引用

IEEE 25th international conference on intelligent Transportation Systems (ITSC)

作者： Xiu, Cong Zhan, Shuguang Peng, Qiyuan Southwest Jiaotong Univ Sch Transportat & Logist Chengdu 610031 Peoples R China Hefei Univ Technol Sch Automot & Transportat Engn Hefei 230009 Peoples R China

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

this study focuses on utilizing multi-source data for metro passenger flow prediction in a practically and computationally efficient way. To further improve the accuracy and reliability of passenger prediction, a deep learning-based framework is proposed in this paper considering the practical interference of neighbor spatial dependence and train events, namely a multi-disturbance spatial-temporal causal convolution network (MD-STCN). Specifically, the passenger flow features and train event features of adjacent areas are constructed as initial input to the approach through feature modelling and data fusion. Furthermore, a two-stage spatio-temporal feature selection algorithm is developed to obtain a compact input. Finally, four datasets from Shanghai Metro are tested to verify the validity and feasibility of our MD-STCN.

关键词： data integration Interference Predictive models Prediction algorithms Feature extraction data models Reliability

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A Study on learning and Simulating Personalized Car-Following Driving Style 25

A Study on Learning and Simulating Personalized Car-Followin...

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IEEE 25th international conference on intelligent Transportation Systems (ITSC)

作者： Sheng, Shili Pakdamanian, Erfan Han, Kyungtae Wang, Ziran Feng, Lu Univ Virginia Sch Engn & Appl Sci Charlottesville VA 22904 USA Toyota Motor North Amer InfoTech Labs Mountain View CA 94043 USA

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

automated vehicles are gradually entering people's daily life to provide a comfortable driving experience for the users. the generic and user-agnostic automated vehicles have limited ability to accommodate the different driving styles of different users. this limitation not only impacts users' satisfaction but also causes safety concerns. learning from user demonstrations can provide direct insights regarding users' driving preferences. However, it is difficult to understand a driver's preference with limited data. In this study, we use a model-free inverse reinforcement learning method to study drivers' characteristics in the car-following scenario from a naturalistic driving dataset, and show this method is capable of representing users' preferences with reward functions. In order to predict the driving styles for drivers with limited data, we apply Gaussian Mixture Models and compute the similarity of a specific driver to the clusters of drivers. We design a personalized adaptive cruise control (P-ACC) system through a partially observable Markov decision process (POMDP) model. the reward function with the model to mimic drivers' driving style is integrated, with a constraint on the relative distance to ensure driving safety. Prediction of the driving styles achieves 85.7% accuracy with the data of less than 10 car-following events. model-based experimental driving trajectories demonstrate that the P-ACC system can provide a personalized driving experience.

关键词： Adaptation models Computational modeling Process control Reinforcement learning Markov processes Safety Trajectory

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Leveraging Artificial Intelligence for Simulating Traffic Signal Strategies 25

Leveraging Artificial Intelligence for Simulating Traffic Si...

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IEEE 25th international conference on intelligent Transportation Systems (ITSC)

作者： Bhatnagar, Saumya Guo, Rongge McCabe, Keith McCluskey, thomas L. Scala, Enrico Vallati, Mauro Univ Huddersfield Huddersfield W Yorkshire England Simplifai Syst Ltd Huddersfield W Yorkshire England Univ Brescia Brescia Italy

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

To support traffic authorities in the assessment of traffic signal strategies via simulation, we propose an approach that leverages on the strengths of automated planning knowledge models to generate accurate traffic simulators. By exploiting the sensors' readings of adaptive traffic control systems in operation in a region of interest, and the conciseness of planning knowledge models, the proposed approach can effectively simulate the impact that traffic signal strategies will have on the considered urban region. Our experimental analysis, performed using real-world historical data, shows that the accuracy of our simulated traffic conditions is within 10% of what was actually recorded by deployed sensors.

关键词： Knowledge engineering Adaptation models Traffic control data models Sensor systems Planning intelligent transportation systems

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automated Driver Drowsiness Detection from Single-Channel EEG Signals Using Convolutional Neural Networks and Transfer learning 25

Automated Driver Drowsiness Detection from Single-Channel EE...

引用

IEEE 25th international conference on intelligent Transportation Systems (ITSC)

作者： Ghadami, Ali Mohammadzadeh, Mohammad Taghimohammadi, Mohammadreza Taheri, Alireza Sharif Univ Technol Dept Mech Engn Tehran Iran

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

One of the major causes of road accidents is drowsy driving. Identifying drivers' drowsiness state is a crucial step in preventing accidents. the state of drowsiness of a driver can be assessed by analyzing the brain's electrical activity using electroencephalogram (EEG) signals. In this paper, the performance of pre-trained neural networks for automatic drowsiness detection using single-channel EEG spectrogram on PhysioNet sleep-EDF dataset is investigated. In addition, we propose a 1D-CNN to take advantage of the time-domain features of the EEG signals to improve the accuracy of the predictions. the performance and robustness of the proposed model are further improved using averaging and stacking fusion methods. Results of this study show that the proposed stackingaverage fusion method provides the best level of accuracy (90.73%) for cross-subject test data. the proposed model simultaneously utilizes both the time-domain and frequencydomain features of EEG data, improving the automatic assessment of drowsiness.

关键词： Convolutional Neural Networks (CNN) Driver Drowsiness Transfer learning Fusion methods

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automated data-Driven Hints for Computer Programming Students 17

Automated Data-Driven Hints for Computer Programming Student...

引用

25th ACM international conference on User Modeling, Adaptation and Personalization (UMAP)

作者： Chow, Sammi Yacef, Kalina Koprinska, Irena Curran, James Univ Sydney Sch Informat Technol Sydney NSW Australia Univ Sydney Grok Learning Sydney NSW Australia

ISBN: (纸本)9781450350679

Formative feedback is essential for learning computer programming but is also a challenge to automate because of the many solutions a programming exercise can have. Whilst programming tutoring systems can easily generate automated feedback on how correct a program is, they less often provide some personalised guidance on how to improve or fix the code. In this paper, we present an approach for generating hints using previous student data. Utilising a range of techniques such as filtering, clustering and pattern mining, four different types of data-driven hints are generated: input suggestion, code-based, concept and pre-emptive hints. We evaluated our approach with data from 5529 students using the Grok learning platform for teaching programming in Python. the results show that we can generate various types of hints for over 90% of students with data from only 10 students, and hence, reduce the cold-start problem.

关键词： intelligent teaching systems personalised feedback educational data mining

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Prediction Of Power Demand And Factor Identification Using Sparse Modeling AI With Ensemble learning For Improvement Of Accuracy 13

Prediction Of Power Demand And Factor Identification Using S...

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Joint 13th international conference on Soft Computing and intelligent Systems and 25th international Symposium on Advanced intelligent Systems, SCIS and ISIS 2024

作者： Hachiya, Tatsuma Kamei, Keiji Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan Nishinippon Institute of Technology Faculty of Engineering Fukuoka Japan

ISBN: (纸本)9798350373332

We have been using sparse modeling AI to identify factors and predict in power demand. In this research, we applied ensemble learning to further improve the accuracy by integrating the results of multiple models. Experimental results showed that sparse modeling AI identified the demand factors, and ensemble learning achieved an average absolute prediction error of 7.5%. © 2024 IEEE.

关键词： Demand response

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Detect Students' Academic Emotions in Classroom: Measurement, Self-perception and Manifested Behaviors 25

Detect Students' Academic Emotions in Classroom: Measurement...

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25th international conference on Computers in Education (ICCE) - Technology and Innovation - Computer-Based Educational Systems for the 21st Century

作者： Liu, Yan Hu, Menghua Gu, Xiaoqing East China Normal Univ Dept Educ Informat Technol Shanghai Peoples R China

ISBN: (纸本)9789869401265

the emotions students experience in the classroom are likely to influence their learning in school. For purposes of helping student improve their learning, this study seeks to further the development of the use of automated analysis of observed data to understand students' emotions and during their learning activities. this study looks at linking behavioral data about emotions with self-report data about emotions with the intent to determine if observed behaviors are able to predict student's emotions. the ICT-supported learning Emotion Scale was used for 57 students to measure their learning emotion while learning in an ICT environment, meanwhile, classroom observations were conducted for 57 students for six weeks in 4 classrooms, paying close attention to student emotional behaviors and emotional changes. the results found that three key groups of behavioral indicators happened frequently. One of the key findings was that students who volunteered to answer questions in class were observed to have an observable "enjoy" emotion 84.79% of the time. the discussion highlighted that the bridge function of students' emotion engagement between classroom observation and questionnaire data.

关键词： Emotional engagement learning analytics Behavioral indicators Observations and questionnaire

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