The document provides an introduction to the Intelligent Transportation Systems Center at Wuhan University of Technology in China. The center was established in 2000 and conducts research to improve traffic safety, reduce emissions and save energy. It has over 50 graduate students and professors working on projects funded by the Chinese government. The center has developed several platforms for research including a driving simulation system, a vehicle for monitoring traffic safety, and a traffic simulation platform.
Sathaporn Opasanon is an assistant professor at Thammasat University specializing in logistics, supply chain management, and transportation engineering. His research interests include network optimization, multiple criteria analysis, and intelligent transportation systems. He holds a PhD from the University of Maryland and has published numerous papers on topics related to his research interests.
Feature Selection Method For Single Target Tracking Based On Object Interacti...IJERA Editor
For single-target tracking problem Kernel-based method has been proved to be effective. A tracker which takes advantage of contextual information to incorporate general constraints on the shape and motion of objects will usually perform better when compare to the one that does not exploit this information. This is due to the reason that a tracker designed to give the best average performance in a variety of scenarios can be less accurate for a particular scene than a tracker that is attuned (by exploiting context) to the characteristics of that scene. The use of a particular feature set for tracking will also greatly affect the performance. Generally, the features that best discriminate between multiple objects and, between the object and background are also best for tracking the object.
This document describes a vehicle accident prevention control system project done by students at the University of Central Punjab under the supervision of Mohsin Manzoor. The project aims to reduce accidents at road turns and curves by using ultrasonic sensors and LED lights to alert drivers of oncoming vehicles. The system would detect vehicles approaching turns and bends in the road and signal to drivers through LED lights. If implemented along the entire route from Jaranwala to Faisalabad, it could help reduce accidents and save lives. The document provides details of the various hardware components used in the project like Arduino boards, sensors, lights and wiring. It also explains the working principle, potential applications and scope for future work.
This document summarizes discussions from a series of 2010 regional summits focused on transportation workforce development. The summits identified common themes such as the need to update transportation curricula to attract diverse students and foster innovation, strengthen links between research and workforce needs, and establish a national clearinghouse of transportation workforce information and resources. The summits also highlighted gaps in recruiting and retaining women and underrepresented groups in transportation professions.
This document summarizes the agenda for a 2010 CUTC Summer Meeting held in College Station, Texas. The agenda included discussions on managing UTC grant budgets without new authorization, coordinating Rip data with state DOTs, communicating the UTC message more effectively, reporting performance indicators, and leveraging accomplishments from other federal awards. Presentations were also given by USDOT operating administrations. Sessions focused on developing partnerships, transportation workforce development, achieving research excellence, and new media applications for UTCs.
This document lists participants for the CUTC Summer Meeting 2010. It includes the names, titles, affiliations for UTCs and home institutions/organizations of over 70 participants. The participants represent UTCs, universities, and government organizations from across the United States.
The Integrated Disease Surveillance Project (IDSP) is a decentralized, state-based project that aims to establish a disease surveillance system for timely public health action. It integrates disease surveillance at state and district levels, improves laboratory support, and provides training. The IDSP oversees surveillance of diseases like malaria, diarrhea, tuberculosis, measles, and more. It has a strong organizational structure from the national to district levels to monitor diseases and respond to outbreaks. The IDSP reporting system utilizes forms to report suspect, probable and confirmed disease cases weekly from health centers to the state and national levels.
The document describes the China Bus System of the Future (CBSF) initiative, which aims to develop an intelligent autonomous new energy public bus system. It provides details on:
1) The CBSF is led by Intelligent Transport System China and implemented in numerous cities with partnerships between government, universities, and companies like Haylion and bus manufacturers.
2) Haylion is the lead technology developer focusing on areas like environment cognition, positioning, and safety systems using sensors and data fusion.
3) The initiative aims to launch trials of autonomous bus systems in cities like Shenzhen to demonstrate technologies like wireless charging infrastructure, V2X communication, and multi-sensor data processing.
Sathaporn Opasanon is an assistant professor at Thammasat University specializing in logistics, supply chain management, and transportation engineering. His research interests include network optimization, multiple criteria analysis, and intelligent transportation systems. He holds a PhD from the University of Maryland and has published numerous papers on topics related to his research interests.
Feature Selection Method For Single Target Tracking Based On Object Interacti...IJERA Editor
For single-target tracking problem Kernel-based method has been proved to be effective. A tracker which takes advantage of contextual information to incorporate general constraints on the shape and motion of objects will usually perform better when compare to the one that does not exploit this information. This is due to the reason that a tracker designed to give the best average performance in a variety of scenarios can be less accurate for a particular scene than a tracker that is attuned (by exploiting context) to the characteristics of that scene. The use of a particular feature set for tracking will also greatly affect the performance. Generally, the features that best discriminate between multiple objects and, between the object and background are also best for tracking the object.
This document describes a vehicle accident prevention control system project done by students at the University of Central Punjab under the supervision of Mohsin Manzoor. The project aims to reduce accidents at road turns and curves by using ultrasonic sensors and LED lights to alert drivers of oncoming vehicles. The system would detect vehicles approaching turns and bends in the road and signal to drivers through LED lights. If implemented along the entire route from Jaranwala to Faisalabad, it could help reduce accidents and save lives. The document provides details of the various hardware components used in the project like Arduino boards, sensors, lights and wiring. It also explains the working principle, potential applications and scope for future work.
This document summarizes discussions from a series of 2010 regional summits focused on transportation workforce development. The summits identified common themes such as the need to update transportation curricula to attract diverse students and foster innovation, strengthen links between research and workforce needs, and establish a national clearinghouse of transportation workforce information and resources. The summits also highlighted gaps in recruiting and retaining women and underrepresented groups in transportation professions.
This document summarizes the agenda for a 2010 CUTC Summer Meeting held in College Station, Texas. The agenda included discussions on managing UTC grant budgets without new authorization, coordinating Rip data with state DOTs, communicating the UTC message more effectively, reporting performance indicators, and leveraging accomplishments from other federal awards. Presentations were also given by USDOT operating administrations. Sessions focused on developing partnerships, transportation workforce development, achieving research excellence, and new media applications for UTCs.
This document lists participants for the CUTC Summer Meeting 2010. It includes the names, titles, affiliations for UTCs and home institutions/organizations of over 70 participants. The participants represent UTCs, universities, and government organizations from across the United States.
The Integrated Disease Surveillance Project (IDSP) is a decentralized, state-based project that aims to establish a disease surveillance system for timely public health action. It integrates disease surveillance at state and district levels, improves laboratory support, and provides training. The IDSP oversees surveillance of diseases like malaria, diarrhea, tuberculosis, measles, and more. It has a strong organizational structure from the national to district levels to monitor diseases and respond to outbreaks. The IDSP reporting system utilizes forms to report suspect, probable and confirmed disease cases weekly from health centers to the state and national levels.
The document describes the China Bus System of the Future (CBSF) initiative, which aims to develop an intelligent autonomous new energy public bus system. It provides details on:
1) The CBSF is led by Intelligent Transport System China and implemented in numerous cities with partnerships between government, universities, and companies like Haylion and bus manufacturers.
2) Haylion is the lead technology developer focusing on areas like environment cognition, positioning, and safety systems using sensors and data fusion.
3) The initiative aims to launch trials of autonomous bus systems in cities like Shenzhen to demonstrate technologies like wireless charging infrastructure, V2X communication, and multi-sensor data processing.
This document proposes a vehicle speed control system based on zone detection. The system uses RFID technology to detect restricted speed zones like hospitals and schools. When a vehicle enters one of these zones, the system reduces and maintains the vehicle's speed at a cutoff speed until it exits the zone. The goal is to automatically control speeds in specific areas to improve road safety. A transmitter would be installed in restricted zones and a receiver in vehicles to regulate speeds. The document outlines this problem, provides an abstract, introduces the system, surveys relevant literature, and lists reference papers on related topics like computer vision for traffic analysis and embedded vehicle speed control systems.
This document provides information about documenting research outputs and outcomes in the Research Hub database maintained by the U.S. Department of Transportation. It defines outputs as tangible research products like technologies, databases, or equipment, and outcomes as impacts of the research outside of academia like adoption of practices, safety impacts, or effects on decision-making. Examples of outputs and outcomes are given from transportation research projects. Instructions are provided for submitting descriptions of outputs and outcomes from active and past projects to the Research Hub database.
Kasra Mokhtari is a PhD candidate in mechanical engineering at Penn State University. His research focuses on incorporating social information into autonomous vehicle decision making using machine learning techniques like deep reinforcement learning. He has developed algorithms for pedestrian collision avoidance and risk-aware path planning. Mokhtari has published several papers in conferences and journals and has relevant work experience as a graduate research assistant developing models for pollutant estimation and autonomous systems risk evaluation.
Presentation by Tyron Louw at 2nd SIP-adus Workshop on Connected and Automated Driving Systems, Tokyo, October 2015
www.sip-adus.jp/workshop/program/speaker/profile/hf/linton.html
This document describes a proposed situational center for transportation called "Transport". It would use various information sources like GPS, traffic cameras, weather data to analyze transportation conditions. It aims to intelligently process this heterogeneous data to better support decision making. This would allow creating an integrated system to manage transportation infrastructure and rapidly develop solutions. It outlines the team involved including their backgrounds in transportation, automation, and complex system control. Finally, it provides a development plan indicating the funding and timeline needed to create a prototype center for testing in the Kaliningrad region by 2014.
Collision avoidance research has focused on vehicle-to-vehicle (v2v), vehicle-to-road (v2r), and road-to-road (r2r) communication. V2v technologies use radar, cameras, or radio to prevent collisions, while v2r systems provide intersection warnings. R2r systems independently sense vehicle information in real-time. Several US universities are conducting intersection collision avoidance research projects using sensors and wireless technologies, though relying solely on vehicle equipment has drawbacks. Alternative approaches use road sensors transmitting traffic data to a base station for predictive collision analysis and warnings. However, current routing implementations result in unacceptable message latency for collision avoidance. A commercial product uses sensors and wireless access points but suffers
The document describes a proposed situational center for transport called "Transport" that would integrate information from various transportation systems and emergency services to provide real-time traffic and transportation updates. It would collect data from navigation systems, traffic detectors, weather services, and individuals to analyze the transportation situation and support decision making. The center would utilize hybrid intelligent systems to process diverse data sources and help transition to a new level of situational analysis. A team is proposed to lead the development, and a development plan and funding needs are outlined to create and test a prototype for the system.
The document summarizes activities of the Infrastructure Systems Committee (ISC). It discusses how the ISC recently completed its membership reorganization and established subcommittees. It then highlights some of the subcommittees and their chairs. It provides details on the committee meeting during a recent TRB conference and recognizes leadership support. It profiles a committee member, their research interests and accomplishments. It also advertises for content to include in future committee newsletters.
This document summarizes a study on delivery systems in Indian university campuses. The study used methods such as field studies, interviews, surveys and workshops to understand the current delivery system and identify pain points from the perspectives of students and delivery agents. It found issues with the last mile of delivery on campuses. The study also examined the need for improved on-campus mobility systems. It proposed a mutual-aid delivery service using intelligent technologies to address the gaps in the current system and better meet user needs.
Traffic Management And Information Control Centre (TMICC)WaseemAhmad186
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TTI’s Connected and Automated Vision for the Future
The Texas A&M Transportation Institute (TTI) shares an industry vision where no vehicles collide and people can use connected and automated transportation to transform how they live, work and interact with their environment. To achieve this vision, research, development and testing are needed on how vehicles, users and transportation infrastructure all work together. While automated vehicles are emerging and connected vehicle research is progressing, TTI believes the most significant gains in safety and mobility will occur at the nexus of these areas. TTI is creating a world-class research environment on the Texas A&M University campus where researchers can collaborate, new transportation paradigms can be created, and future mobility and safety can be showcased.
1. The document proposes a non-invasive contact technique using MEMS sensors to automatically control the speed of trains. It involves placing a wireless sensing system on trains to monitor speed without physical contact.
2. The existing methods use infrared cameras or detectors which increase costs and power consumption. Contact methods are costly and require stopping trains. The proposed system uses low-cost, self-powered accelerometers for precise speed monitoring and control.
3. The system aims to precisely adjust train speed for safety using a battery-powered microcontroller and sensing platform without disrupting rail operations.
This technical seminar presentation provides an overview of Intelligent Transportation Systems (ITS). ITS uses advanced technologies like wireless communications, sensors, and computational systems to improve traffic flow, reduce congestion, and enhance safety. The presentation defines ITS and its components, classifies different types of ITS including advanced traffic management systems, electronic toll collection, and advanced traveler information systems. It discusses the benefits of ITS in reducing congestion and accidents while saving lives, time, and money. The presentation also provides context on implementing ITS in India to improve public transportation and build infrastructure more efficiently.
Real time path planning based on hybrid-vanet-enhanced transportation systemPvrtechnologies Nellore
The document presents a real-time path planning algorithm for vehicles based on a hybrid vehicular ad hoc network (VANET) and cellular network system. The algorithm aims to improve both the overall road network utilization and reduce the average vehicle travel cost. It first establishes a communication framework using VANETs, cellular networks, vehicles, roadside units and a traffic server. It then proposes a path planning algorithm based on Lyapunov optimization that considers both network performance and driver preferences like travel time and distance. Simulation results show the algorithm can efficiently find alternative paths for vehicles to bypass congestions while reducing travel costs.
Anthony DePrator is seeking a career in traffic engineering and transportation safety. He has a Master's degree in transportation engineering from Penn State University and conducted research on dynamic left-turn restrictions. He has over 3 years of work experience including research assistant roles developing traffic simulation models and safety analysis, as well as internships with PennDOT and GE.
A Static Traffic Assignment Model Combined With An Artificial Neural Network ...Anita Miller
The document discusses the development of a combined system that integrates an artificial neural network (ANN) delay model with a traffic assignment model to estimate intersection delays and route choices simultaneously. The ANN delay model is trained using extensive simulations based on TRANSYT-7F signal optimizations to estimate delays for different intersection types. A combined iterative optimization and assignment procedure is then developed to achieve a convergent traffic assignment solution that considers the interaction between traffic routing and signal controls.
A Systematic Literature Review Of Vehicle Speed Assistance In Intelligent Tra...Nat Rice
This document summarizes a systematic literature review of vehicle speed assistance systems in intelligent transportation systems. It identified 79 primary studies published between 2011-2020. After applying quality assessment criteria, 50 studies were selected for detailed analysis. The review found that vehicle speed assistance systems aim to achieve various driving goals like eco-driving, safety, comfort and travel time improvement. It analyzed the different methods proposed in the literature to provide speed assistance and the objectives addressed by these systems. The review identified challenges and opportunities for future research in intelligent vehicle speed assistance.
This document discusses a partnership between Go! / ¡Vamos!, a magazine aimed at attracting teens to careers in transportation, and University Transportation Centers (UTCs). It proposes ways the two organizations can work together, such as UTCs sponsoring issues of the magazine focused on their research areas and student programs. The magazine would also profile UTC students and showcase each center's opportunities to help UTCs recruit more students and increase diversity in the transportation workforce. Contact information is provided for the magazine's editors to discuss potential collaboration.
- Texas A&M University has over 48,000 students total with over 10,000 enrolled in the College of Engineering. It is a top 20 research university.
- The Texas A&M University System includes multiple universities across Texas. The Texas Transportation Institute (TTI) is a part of this system and conducts research to support the Texas Department of Transportation.
- TTI has developed many products that have been commercialized like crash cushions and bridge testing technology. It trains many transportation professionals and its research provides over a 5 to 1 benefit cost ratio for Texas.
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This document proposes a vehicle speed control system based on zone detection. The system uses RFID technology to detect restricted speed zones like hospitals and schools. When a vehicle enters one of these zones, the system reduces and maintains the vehicle's speed at a cutoff speed until it exits the zone. The goal is to automatically control speeds in specific areas to improve road safety. A transmitter would be installed in restricted zones and a receiver in vehicles to regulate speeds. The document outlines this problem, provides an abstract, introduces the system, surveys relevant literature, and lists reference papers on related topics like computer vision for traffic analysis and embedded vehicle speed control systems.
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1. The document proposes a non-invasive contact technique using MEMS sensors to automatically control the speed of trains. It involves placing a wireless sensing system on trains to monitor speed without physical contact.
2. The existing methods use infrared cameras or detectors which increase costs and power consumption. Contact methods are costly and require stopping trains. The proposed system uses low-cost, self-powered accelerometers for precise speed monitoring and control.
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Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
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GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
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* Importance and benefits of vector search
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* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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Mind map of terminologies used in context of Generative AI
ITS Presentation by Weifeng Wang - Wuhan University, China
1. Introduction to Research on
Intelligent Transportation Systems
in WUT
Weifeng Wang
Wuhan University of Technology
wangweifeng100@126.com
June, 2010
2. Outlines
Basic Information
Research Fields
Current Projects
Research Achievements
Intelligent Transportation Systems Center, Wuhan University of Technology, China
3. Profile of WUT
A Chinese key university under the direct administration of
Ministry of Education
One of the first universities constructed in priority by the
“State 211 Project” for Chinese higher education institutions
Merged on May 27th 2000 by the authorization of the State
Council, from the former Wuhan University of Technology,
Wuhan Transportation University and Wuhan Automotive
Polytechnic University
Intelligent Transportation Systems Center, Wuhan University of Technology, China
4. Profile of WUT
Development Vision
To build the university into a first-class Chinese university
with some disciplines into world advanced level both in
research and education with the characteristics of being
open, multi-disciplinary and research-centered.
Intelligent Transportation Systems Center, Wuhan University of Technology, China
5. Profile of WUT
Two campuses
Ma Fangshan Campus and Yu Jiatou Campus
Yu Jiatou Campus
Ma Fangshan Campus
New Campus
Intelligent Transportation Systems Center, Wuhan University of Technology, China
6. Profile of WUT
Faculty member: 3,005
Professors : 540
Associate Professors: 1,163
Academicians of CAS: 3
Academicians of CAE: 3
Undergraduate Students: 35,816
Postgraduate Students: 12,675
International Students: 260
Registered Students of Online Education: 29,896
Registered Students of Vocational Education: 8,305
Intelligent Transportation Systems Center, Wuhan University of Technology, China
7. Disciplines and Schools
Disciplines of WUT :
Engineering
Natural Sciences
Management 7 state key subjects
Economics 79 specialties for undergraduates
Literature & Arts
132 master-degree programs
Law
61 doctoral degree programs
Philosophy
History MBA, MPA, MFA
Education
Medicine
Intelligent Transportation Systems Center, Wuhan University of Technology, China
8. History of ITSC
Intelligent Transportation Systems Center (ITSC) is a
research institute in Wuhan University of Technology.
Established in 2000 by Wuhan University of
Technology, China
Authorized as the Engineering Research Center
for Transportation Safety in 2006 by Ministry of
Education, China
Now developed one of the most influential
transportation institute in China
Intelligent Transportation Systems Center, Wuhan University of Technology, China
9. Orientation of ITSC
Fundamental Research Applied Technology
Highway Transportation & Waterway Transportation
Software Development Equipment Design
Intelligent Transportation Systems Center, Wuhan University of Technology, China
10. Objectives of ITSC
It is why we pursue research every day.
Improve traffic safety
Reduce vehicle emission
Save transportation energy
Intelligent Transportation Systems Center, Wuhan University of Technology, China
11. Research Team of ITSC
Director: Prof Xinping YAN
Professors: 4
Associate Professors: 3
Research Associate: 2
Postdoctoral: 3
Technician: 1
Prof Xinping YAN
Graduate Students: 52
Intelligent Transportation Systems Center, Wuhan University of Technology, China
12. Published Journal of ITSC
Name: Journal of Transportation Information and Safety
Editor: Prof Xinping YAN
Scope: Technical and professional articles on the
safety, planning, design, construction,
maintenance, and operation of waterway,
highway, rail, and urban transportation, as
well as pipeline facilities for water, oil, and gas.
Intelligent Transportation Systems Center, Wuhan University of Technology, China
13. Outlines
Basic Information
Research Fields
Current Projects
Research Achievements
Intelligent Transportation Systems Center, Wuhan University of Technology, China
14. Field 1 - Highway
Traffic safety control and supply
Vehicle emission control
Transportation energy saving
Traffic information control and guidance
Intelligent Transportation Systems Center, Wuhan University of Technology, China
15. Field 2 - Waterway
Traffic information collection and equipment
Traffic accident simulation
Traffic safety early-warning and emergency
treatment
Intelligent Transportation Systems Center, Wuhan University of Technology, China
16. Outlines
Basic Information
Research Fields
Current Projects
Research Achievements
Intelligent Transportation Systems Center, Wuhan University of Technology, China
17. The Projects of ITSC
Lateral Control Model for Intelligent Vehicles
(Funded by Ministry of Science and
Technology, China)
The Factors of Road Traffic Accidents Based on
Multi-analysis Technique (Funded by Ministry of
Science and Technology, China)
Pre-warning System of Transportation Safety
(Funded by National Basic Research Program
of China)
Intelligent Transportation Systems Center, Wuhan University of Technology, China
18. The Projects (Cont’d)
Driver Visibility Improvement Technology
(Funded by National Natural Science
Foundation of China)
Information Theory and Method for Automated
Highway System (Funded by Ministry of
Education, China)
Mechanism of Pavement Texture versus Skid
Resistance (Funded by National Natural
Science Foundation of China)
Intelligent Transportation Systems Center, Wuhan University of Technology, China
19. Outlines
Basic Information
Research Fields
Current Projects
Research Achievements
Intelligent Transportation Systems Center, Wuhan University of Technology, China
20. Integrated Driving
Simulation Platform (IDSP)
Basic
Intelligent Transportation Systems Center, Wuhan University of Technology, China
21. Functions of IDSP
Evaluate traffic safety
Detect driving fatigue
Monitor driving behavior
Evaluate effect of driving behavior on traffic flow
Evaluate response of driving behavior to traffic
information
Evaluate performance of driving safety assistance
system
Intelligent Transportation Systems Center, Wuhan University of Technology, China
22. Multifunctional Vehicle
for Traffic Safety (MVTS)
Eye Tracking
GPS
System
Vehicle-mounted
Computer Pavement
Detection
System
Vehicle Infrastructure Driving Behavior
Integration Surveillance Physiological
Tactile detector
Detector
Intelligent Transportation Systems Center, Wuhan University of Technology, China
23. Functions of MVTS
Integrate vehicle and infrastructure
Survey driving behavior
Detect pavement skid resistance
Intelligent Transportation Systems Center, Wuhan University of Technology, China
24. Traffic Simulation
Platform (TSP)
Intelligent Transportation Systems Center, Wuhan University of Technology, China
25. Functions of TSP
Simulate traffic operation
Collect and distribute traffic information
Vehicle-to-Infrastructure communication
Automatic driving development
Intelligent Transportation Systems Center, Wuhan University of Technology, China
26. Automatic Identification
System on Vessels
What is the AIS?
The Automatic Identification System (AIS) is a short
range coastal tracking system used on ships and by
Vessel Traffic Services (VTS) for identifying and
locating vessels by electronically exchanging data
with other nearby ships and VTS stations.
Intelligent Transportation Systems Center, Wuhan University of Technology, China
27. AIS Prototype
Information such as unique identification, position,
course, and speed can be displayed on the screen.
8cm
12cm
18cm
Intelligent Transportation Systems Center, Wuhan University of Technology, China
28. Software Interface of AIS:
A case of the Yangtse River
Intelligent Transportation Systems Center, Wuhan University of Technology, China
29. Vehicle Management System
Information such as speed, position, driver and
emergency can be monitored.
Intelligent Transportation Systems Center, Wuhan University of Technology, China
30. Traffic Emergency
Management System (TEMS)
Why we need TEMS?
Normal traffic
Abnormal traffic
Intelligent Transportation Systems Center, Wuhan University of Technology, China
31. Software Interface of TEMS:
A case of the 6th Intercity Games
Intelligent Transportation Systems Center, Wuhan University of Technology, China
32. Pavement Detection Vehicle
Function:
Improve visibility under adverse
weather
Measure pavement roughness
Evaluate pavement skid
resistance
Detect pavement crack
Intelligent Transportation Systems Center, Wuhan University of Technology, China
33. Welcome to
Intelligent Transportation Systems Center,
Wuhan University of Technology, China.
http:// itsc.whut.edu.cn
Thanks for your kind attention
Intelligent Transportation Systems Center, Wuhan University of Technology, China