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Keynote speech 3: Big data Computing and Machine Learning for Intelligent Transportation and Connected Vehicles
- Source :
- 2020 Fifth International Conference on Fog and Mobile Edge Computing (FMEC).
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- We are developing machine learning algorithms and software to fuse real-time feeds from video cameras and traffic sensor data to generate real-time detection, classification, and space-time trajectories of individual vehicles and pedestrians. This information is then transmitted to a cloud-based system and then synthesized to create a real-time city-wide traffic palette. I will discuss our research on: Smart intersections: Space-time trajectories are used to understand and improve the safety and efficiency of the intersection. Using conflict points of the vehicle-pedestrian trajectories, we identify potential collisions, or “near-misses,” and how they are related to the state of the signal cycle (transition from green to yellow, from yellow to red, etc.) and the presence of other vehicles and pedestrians. • Smart system: We are developing efficient signal re-timing for different corridors by time of day and day of the week to reflect the changes in network demand. We are also developing machine learning techniques for real-time detection of incidents and accidents on arterial networks. • Smart interactions with connected and autonomous vehicles: We have developed signalized intersection control strategies and sensor fusion algorithms for jointly optimizing vehicle trajectories and signal control for a mixture of autonomous vehicles and traditional vehicles at every intersection
Details
- Database :
- OpenAIRE
- Journal :
- 2020 Fifth International Conference on Fog and Mobile Edge Computing (FMEC)
- Accession number :
- edsair.doi...........3e84d625aa171964526ab17288e5afb8