2017 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (IEEE CIVTS' 17)
The research and development of intelligent vehicles and transportation systems are rapidly growing worldwide. Intelligent transportation systems are making transformative changes in all aspects of surface transportation based on vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I) connectivity, and automated driving (AV). In addition with the decreasing sensor costs and computer chips, and increasing computing power and data storage capacity, it has become practical to build a host of intelligent devices in cars that can be used in airbag control, unwelcome intrusion detection, collision warning and avoidance, power management and navigation, driver alertness monitoring etc. Computational intelligence plays a vital role in building all types and levels of intelligence in vehicle and transportation systems.
The objective of this symposium is to provide a forum for researchers and practitioners to present advanced research in computational intelligence with a focus on innovative applications to intelligent vehicle and transportation systems. This symposium seeks contribution on the latest developments and emerging research in all aspects of intelligent vehicle and transportation systems.
- Advanced transportation information, communication and management systems
- Air, road, and rail traffic management
- Automated driving and driverless car
- Cloud computing and big data in transportation and vehicle systems
- Collision detection and avoidance
- Connected vehicles of the future
- Driver assistance and automation systems
- Driver state detection and monitoring
- Learning and adaptive Control
- Multimodal intelligent transport systems and services
- Object recognitions such as pedestrian detection, traffic sign detection and recognition
- Personalized driver and traveler support systems
- Pervasive and ubiquitous computing in logistics
- Route guidance systems
- Simulation and forecasting models
- Spatio-temporal traffic pattern recognition
- Trip modeling and driver speed prediction
- Vehicle communications and connectivity
- Vehicle fault diagnostics and health monitoring
- Vehicle energy management and optimization in hybrid vehicles
(To be announced)
Yi Lu Murphey
University of Michigan-Dearborn, Dearborn, MI 48128, USA.
Ford Motor Company, USA.
Federal do Rio Grande do Sul, Brazil.
Nanyang Technological University, Singapore.
University of Stellenbosch, Stellenbosch, South Africa, 7602 South Africa.
U.S. Army Tank Automotive Research, Development, and Engineering Center (TARDEC),
Detroit, MI 48397-5000, USA.
Ford Motor Company
Shanghai Jiao Tong University, China.
- Majid Ahmadi, Ph.D. Professor, Department of Electrical and Computer Engineering, University of Windsor, Windsor, Ontario, Canada. firstname.lastname@example.org
- Hafiz Malik, Ph.D. Associate Professor,
- Yinghao Huang, Ph.D., Research Engineer, Verizon, NJ, USA
- Chaomin Luo, PhD, Associate Professor, Department of Electrical and Computer Engineering, University of Detroit Mercy, Michigan, USA. email@example.com
- Jungme Park, Ph.D. Research Scientist, Changan US R&D Center, USA
- Ishwar K Sethi, Ph.D. Professor, School of Engineering and Computer Science, Oakland University, Rochester, MI 48098, USA. firstname.lastname@example.org
- Tatiana Tambouratzis, Ph.D., Associate Professor, University of Piraeus, Greece, email@example.com
- Alper Kursat Uysal, Ph.D. Assistant Professor, Computer Engineering Department, Anadolu University, firstname.lastname@example.org
- Paul Watta, Ph.D., Associate Professor, Department of Electrical and Computer Engineering, the University of Michigan-Dearborn, Dearborn, MI 48128, USA. email@example.com
- Weiwei Zhang, Ph.D, Lecturer, Shanghai University of Engineering Science, firstname.lastname@example.org