At present, pollution, congestion and traffic related problems are becoming major societal problems globally. Consequently, new automotive solutions and more suitable transportation modes are urgently required in order to provide various benefits for citizens and road users living better in the cities, including enhanced traffic safety, reduced pollution and emissions, and better utilization of road networks and emergency social resources. Along this line, many innovations and technologies have been invented for making vehicles electric, connected, shared and autonomous in the new context of modern intelligent transportation systems.
One of the core ideas to make intelligent transportation systems work effectively is to deploy advanced communication, coordination and many IoT enabling technologies among vehicles, road infrastructure, as well as the cloud in a flexible and integrated environment. By adopting IoT, useful information from vehicles, drivers and vulnerable road users, e.g. pedestrians and cyclists, can be collected, processed and shared in an effective, secure and timely manner, making the overall transportation system more efficient and reliable. At this point, many new vehicular applications have been developed and implemented using the IoT based solution architecture, which heavily leverages novel algorithms, including machine and deep learning methods, loud/edge computing capabilities, and real-time V2X communication technologies.
Examples include, but not limited to:
- Smart parking system
- Remote vehicle diagnostic system
- E-Call system
- Collision prediction and avoidance system
- Driving monitoring system (e.g. detecting driver fatigue and awareness)
The WF-IoT 2019 track on Automotive and Transportation System will explore the potential of IoT enabling technologies for real world automotive and transportation applications from both a global and Irish perspective. The session will mainly focus on the following topics:
- IoT enabled congestion, pollution and emission control
- IoT based monitoring and safety for vulnerable road users
- IoT technologies for enhanced driving experience
- Green transportation and E-Mobility
- Connected and autonomous vehicles in intelligent transportation
Javier Gozalvez, Universidad Miguel Hernandez de Elche, Spain
Mingming Liu, IBM Ireland Lab – Innovation Exchange
Dr Mingming Liu is a data scientist within Innovation Exchange, IBM Ireland. He has many years of experience in machine learning, data science, centralized & distributed control and optimization theories with strong links to electric, hybrid vehicles and IoT in the context of smart grids, intelligent transportation systems and smart cities. Prior to IBM, he was employed as a postdoctoral researcher then a senior postdoctoral researcher with the control and decision science research group at University College Dublin, Ireland, where he spent almost three years working on both EU and SFI funded projects, including Green Transportation and Networks (SFI) and Enable-S3 (H2020). He is currently involved in several EU H2020 funded projects at IBM, and his works mainly focus on leveraging the state-of-the-art machine learning, deep learning techniques, as well as advanced control & optimization solutions for practical and challenging problems arising in the industry.
Mingming received his B.Eng. degree with first class honours from the Dept. of Electronic Engineering at the National University of Ireland Maynooth in 2011, and then the PhD degree in Applied Mathematics from the Hamilton Institute at the Maynooth university in 2015 with his thesis entitled “Topics in Electromobility and Related Applications”. He is an IEEE member and an active reviewer in the fields. He has published over 20 papers to date, including top-tier journals such as “IEEE Transactions on Smart Grid”, “IEEE Transactions on Intelligent Transportation System”, “IEEE Transactions on Automation Science and Engineering”, and “Automatica”.
Diarmuid McSwiney, Teagasc