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检索条件"机构=Beijing Key Lab of Urban Intelligent Traffic Control Technology"

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Virtual Coupling technology of urban Rail Transit: A Systematic Literature Review

Virtual Coupling Technology of Urban Rail Transit: A Systema...

引用

International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Feng, Li Zhou, Huijuan Beijing Key Laboratory of Intelligent Control Technology for Urban Road Traffic North China University of Technology Beijing China

ISBN: (纸本)9789819639687

urban rail transit is an important mode of public transportation in modern cities, but the dispersion of passenger travel demand and the uneven distribution of time and space are becoming more and more obvious, which contradicts the supply of urban rail transit capacity, which greatly restricts the performance of urban rail network efficiency and the improvement of service level. This paper reviews and analyzes the research on virtual coupling technology of modern urban rail transit trains, and firstly, introduces the research background and basic concept of virtual coupling technology. Secondly, the key technologies required for virtual coupling technology are summarized, including safety protection, high-precision real-time positioning, vehicle technology and efficient transportation organization. Finally, the research directions of train virtual coupling technology are sorted out and analyzed, which are respectively train operation control technology, communication technology and transportation organization technology. © beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： urban transportation

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Analysis of Train Delay Regularity in High Speed Railway

Analysis of Train Delay Regularity in High Speed Railway

引用

International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Ma, Boyi Zhou, Huijuan Beijing Key Laboratory of Intelligent Control Technology for Urban Road Traffic North China University of Technology Beijing China

ISBN: (纸本)9789819639687

In recent years, driven by China’s economic take-off and the robust support of national policies, the high-speed railway has entered the "peak period" of development. Consequently, its transportation demand is steadily escalating, its transportation tasks are perpetually augmenting, and passengers’ demands for service quality are becoming increasingly rigorous. This implies that in high-speed rail transportation, aside from ensuring safety, it is also necessary to guarantee that the train can arrive on time or even "punctually". Nevertheless, during the actual operation of the train, numerous factors that impact the on-time operation of the train are inevitable. Under the influence of various interfering factors, the delay rate of the high-speed train also rises correspondingly. Therefore, resolving the issue of train delays has emerged as one of the significant matters in the optimization of high-speed railway organization. To minimize the influence of train delays on the daily running organization of railway transport, this paper adopts the virus transmission theory as the theoretical foundation and the SIR Model as the model basis to establish a transmission model of train delays occurring at stations. Based on this, three scenarios are simulated to investigate the transmission law of train delays occurring at stations. © beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Railroads

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A traffic Conflict Research Approach Based on Multi-head CNN-LSTM Attention Trajectory Prediction

A Traffic Conflict Research Approach Based on Multi-head CNN...

引用

International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Ren, Meng Zhang, Haibo Fang, Jun Beijing Key Laboratory of Intelligent Control Technology for Urban Road Traffic Beijing China North China University of Technology Beijing100144 China

ISBN: (纸本)9789819639601

To enhance traffic safety in urban road merging areas, this study introduces a traffic conflict prediction method utilizing a Multi-Head CNN-LSTM Attention (CLMHA) trajectory prediction model. The multi-head CNN-LSTM attention model integrates the characteristics of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, employing a multi-head attention mechanism to effectively extract vehicle trajectory features. This approach is particularly adept at addressing the spatial dependencies and temporal dynamics inherent in vehicle trajectory prediction within merging areas characterized by complex environments. To mitigate redundancy in feature data and to obtain complementary features, a convolutional neural network feature fusion technique is proposed, facilitating improved extraction of the spatial and temporal motion relationships of vehicles. The efficacy of the method is validated using the Mirror-traffic dataset. The results indicate that the proposed trajectory prediction algorithm demonstrates superior accuracy in forecasting future vehicle trajectories compared to alternative algorithms, thereby yielding enhanced outcomes in traffic conflict prediction. © beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Convolutional neural networks

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Design and Research of an intelligent Energy-Saving System for Highway Tunnel Lighting

Design and Research of an Intelligent Energy-Saving System f...

引用

International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Zhang, Feng Zhang, Mengwei Wang, Tong Li, Kailong TECH Traffic Engineering Group Co. Ltd. Beijing100083 China Beijing Key Laboratory of Urban Intelligent Traffic Control Technology North China University of Technology Beijing10041 China

ISBN: (纸本)9789819639762

This paper mainly discusses the design and research of an intelligent energy-saving system for highway tunnel lighting. With the increase in highway traffic volume, the demand for tunnel lighting has grown significantly. Traditional lighting systems not only consume high energy but also struggle to meet the requirements under dynamic traffic conditions. Therefore, developing an intelligent lighting energy-saving system is particularly important. This paper first analyzes the basic components of existing highway tunnel lighting systems and their shortcomings, pointing out the limitations of traditional lighting systems in terms of energy efficiency and flexibility. Next, it proposes a design scheme for an intelligent lighting system based on Internet of Things (IoT) technology and big data analysis. This system, by installing smart sensors, can monitor traffic flow and environmental changes in real time, automatically adjusting lighting intensity and operating modes to achieve optimal lighting effects. Additionally, this paper conducts simulation experiments on energy-saving effects, with results showing that the designed intelligent lighting system can achieve energy savings of 20% to 40% compared to traditional systems while meeting safety and visibility requirements. © beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： DC power supply energy saving intelligent control Tunnel lighting technology

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Research on Gear Fault Diagnosis Method Based on CNN-Transformer Modeling

Research on Gear Fault Diagnosis Method Based on CNN-Transfo...

引用

International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Han, Xiangbo Cai, Lulu Wang, Huahua Ma, Zhenliang Gao, Mengqi Beijing Key Laboratory of Intelligent Control Technology for Urban Road Traffic North China University of Technology Beijing100144 China Jiangsu Industrial Innovation Center of Intelligent Equipment Co. Ltd. Liyang213300 China

ISBN: (纸本)9789819639687

Aiming at the problem of complex gear operating environment, difficult fault feature extraction and low accuracy, a deep learning fault diagnosis method based on the combination of convolutional neural network (CNN) and Transformer, which is based on three sensors, three channels, and multivariate data processing fusion, is proposed. The method firstly processes the raw data notational by Fast Fourier Transform (FFT) and Short-Time Fourier Transform (STFT), merges the features after feature extraction of these two data processing methods respectively, and then utilizes the CNN-Transformer neural network model for fault diagnosis of gears. The two dimensions of data processing and deep learning model fusion solve the problem of complementary local and global information processing in fault diagnosis, and realize the accurate classification of gear faults. © beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Convolutional neural networks

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Research on the Grading of Driving Safety Risks Under Low Visibility Conditions on Expressways Based on Driving Simulation

Research on the Grading of Driving Safety Risks Under Low Vi...

引用

International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Zhao, Nale He, Qiao Liu, Ruiche Sun, Ming Research Institute of Highway Key Laboratory of Road Safety Technologies Ministry of Transport People’s Republic of China Beijing100088 China Beijing Key Laboratory of Urban Intelligent Traffic Control Technology North China University of Technology Beijing100144 China

ISBN: (纸本)9789819639762

This study evaluates the safety risks of highways under low visibility conditions by combining visibility and traffic intensity, and utilizing driving simulation experiments. Nine experimental schemes were designed based on different visibility and traffic flow states, and the simulation experiment data of 30 participants were analyzed. The study established an evaluation system covering traffic efficiency and safety, and used the AHP-TOPSIS method along with hierarchical clustering algorithms to categorize risks into three levels, providing theoretical support and decision-making guidance for the safety management of highways. © beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Automobile driver simulators

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Research on Comfortable Braking Based on Multi-layer Information Superposition Intention Discrimination System

Research on Comfortable Braking Based on Multi-layer Informa...

引用

International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Gao, Mengqi Yang, Xiaopeng Yang, Liyu Jin, Yan Ma, Zhenliang Han, Xiangbo Beijing Key Laboratory of Intelligent Control Technology for Urban Road Traffic North China University of Technology Beijing100144 China China North Vehicle Research Institute Beijing100071 China Jiangsu Industrial Innovation Center of Intelligent Equipment Co.Ltd Liyang213300 China

ISBN: (纸本)9789819639687

Improving the comfort of vehicle braking is of great significance to improve the travel experience of drivers and passengers, but the problem is that compared with the dynamic calculation of conventional braking, the calculation difficulty is greatly increased, and at the same time, it is often at the cost of increasing the braking distance and reducing braking safety. In this paper, an intention recognition system based on multi-layer information superposition and a comfortable braking method that works together with the acceleration curve at the end of smooth braking are proposed to further improve the braking comfort of the vehicle and ensure the braking safety. Based on the LSTM neural network trained by the HO algorithm to predict the characteristic values of the braking intention determined by experiments, the driver′s braking intention is accurately identified, and the qualitative characterization is emergency braking or conventional braking. According to the comfortable braking expectation proposed in this paper, the comfortable braking expectation is quantitatively accumulated for all the above information, the comfortable braking expectation caused by safety and traffic efficiency is distinguished by 0 and 1, and the accumulation caused by eigenvalues is accumulated by the quantitative accumulation formula proposed in this paper, and the comfortable braking behavior is stimulated after reaching the threshold: the intention recognition system of multi-layer information superposition identifies the situation that is safe, does not affect the traffic efficiency and conventional braking intention, and smooths the acceleration curve at the end of braking. Based on the Carsim-Simlink platform, three working conditions were built for simulation and experimental verification. The results show that the indicators of comfort and safety are significantly improved, indicating that the method proposed in this paper has an improvement effect on the comfortable braking o

关键词： Eigenvalues and eigenfunctions

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Research on Abnormal Behavior Identification Method for Autonomous Driving Formation Vehicles in urban Road Environment

Research on Abnormal Behavior Identification Method for Auto...

引用

International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Li, Kailong Zhang, Feng Li, Min Wang, Li Beijing Key Laboratory of Urban Intelligent Traffic Control Technology North China University of Technology Beijing10041 China School of Electrical and Automation Engineering East China Jiaotong University Nanchang330100 China Beijing Jidao Technology Co. Ltd. Beijing100044 China

ISBN: (纸本)9789819639687

With the development of science and technology and social progress, the landing of automatic driving technology seems to be close at hand, but the reality is that high-level automatic driving vehicles want to drive safely on urban roads, but also need to have the ability to accurately identify abnormal behaviors and accurately assess the risks in their operation. This paper focuses on the scientific issues in the process of abnormal behavior identification and risk assessment of autonomous driving formation vehicles in urban road environments. We constructed a framework for identifying abnormal behaviors of autonomous driving multi-vehicle formations in urban road environments, simulated urban road traffic scenarios using the CARLA-SUMO joint simulation platform, and completed several groups of independent simulation tests, and created a 17-dimensional multi-vehicle formation dataset by using five typical machine learning methods, and completed the identification and classification of six types of abnormal behaviors: sharp acceleration, sharp deceleration, overspeed, sharp steering, discrete, and falling out of line, abnormal behavior identification and classification. © beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Automatic vehicle identification

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A Digital Human Information Interaction System for intelligent Parking Lots

A Digital Human Information Interaction System for Intellige...

引用

International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Sun, Peize He, Hao Liu, Xuedi Rakhmatov, Utkirjon Golibjon Ugli, Pulatov Sukhrobjon He, Ye Yang, Yang Wang, Pangwei Beijing Key Laboratory of Intelligent Control Technology for Urban Road Traffic North China University of Technology Beijing100144 China Jizzakh Polytechnic Institute Jizzakh130100 Uzbekistan Tashkent State Transport University Street Adilxodjayev 1 Tashkent100067 Uzbekistan

ISBN: (纸本)9789819639649

With the development of the social economy and the improvement of living standards, the number of privately owned vehicles is sustainable increasing, so intelligent parking system is a research direction currently. The manual management and guidance of traditional parking lots has many issues such as low efficiency and poor customer experience. To address these issues, this paper designs an intelligent parking lot system that amalgamates data collection at the site, cloud processing, digital human guidance and interaction. In terms of path planning, it optimizes the algorithm based on reinforcement learning which using a dynamic greedy factor, and further refining existing path planning by reducing the probability of vehicle encounters. The system fills the gap in the application of AI digital human within the field of transportation, and the digital human avatars can provide more friendly communication for users. Meanwhile, it achieves better interactive effects and provides a reliable solution for improving the efficiency of parking lot management. © beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Reinforcement learning

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Design and Implementation of intelligent Connected Vehicle Ecological Driving System Based on Holographic traffic Perception

Design and Implementation of Intelligent Connected Vehicle E...

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International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Yang, Yu Wang, Zongqi Zhang, Yuqi Li, Zexuan Li, Boao Karimova, Kamola Ugli, Nishonov Azizbek Orzikul Zhang, Mingfang Beijing Key Laboratory of Intelligent Control Technology for Urban Road Traffic North China University of Technology Beijing100144 China Tashkent State Transport University Street Adilxodjayev 1 Tashkent100067 Uzbekistan Jizzakh Polytechnic Institute Jizzakh130100 Uzbekistan

ISBN: (纸本)9789819639601

In order to promote the development of green traffic, this paper develops an intelligent connected vehicle ecological driving system that integrates V2X multi-source holographic traffic perception, vehicle detection and tracking, and dynamic path planning for typical urban road scenes. Firstly, this paper used V2X vehicle-road collaborative technology to collect the traffic flow and the motion state information of the vehicle. The least squares dynamic weighted fusion algorithm is introduced to synchronously process the acquired traffic data. Based on safety distance constraint and kinematic constraint, the ecological driving objective function considering operation efficiency and environmental benefit is constructed. Then, the hybrid A* algorithm and the modified RS curve fusion path planning method are used to solve the optimal driving trajectory of intelligent connected vehicles. Finally, a vehicle-mounted intelligent connected vehicle ecological driving system terminal is developed. The optimal ecological driving track, the optimal speed information and early warning information are sent to the driver in real time. The actual scenario verification results show that the driver can obtain the current traffic state, the warning prompt and the optimal suggested speed through the ecological driving system, which achieves the purpose of improving the driving safety and economy. Through the statistics of the energy consumption of intelligent connected vehicles within a week, we found the energy consumption reduction effect is significant, which shows that the proposed system scheme can provide feasible solutions for the future development of urban green intelligent transportation system. © beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Motion planning

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