| 18 | | The goal of this project is to create a method for estimating the statistics for vehicle count/traffic flow into one intersection in New York City. As an example, record videos of the northbound traffic on Amsterdam Avenue, as vehicles are entering the 120th St./Amsterdam Av. intersection. Using YOLOv3 deep learning model, detect and count vehicles as they approach/enter the intersection from south, making sure that there is no double-counting. Use 180 second long video fragments (approximately two traffic light cycles), and repeat up to half a dozen times a day, for a number of workweek/weekend days during the same times of each day. Compare the vehicle count (traffic flow) as a function of the time of the day. Utilize NVIDIA !DeepStream deployed on COSMOS GPU compute servers to run the model. The method should be generalizable/expandable to any direction of vehicle movement, when appropriate camera views are available. |
| | 13 | The goal of this project is to work on development of the WINLAB self driving car simulator. The project includes development of ~1/14 scale vehicles for use as a remote self-driving car testing platform, as well as a virtual simulation environment which will model both the physical vehicles and the testbed environment. Robot Operating System (ROS) will be used for both halves of the project, with the simulation running in Gazebo. |
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| | 15 | There are several objectives for this project: |
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| | 17 | * Design and implementation of additional sensors for existing vehicles to allow for remote experimentation |
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| | 19 | * Incorporation of ROS control into existing car software |
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| | 21 | * Use of AI/machine learning algorithms for self driving behavior |
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| | 23 | Due to current operating status at Rutgers, in-person lab work with the physical hardware will have to wait until later in the summer. |
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