Self-Driving Vehicular Project

Team: Aaron Cruz [UG], Arya Shetty [UG], Brandon Cheng [UG], Tommy Chu [UG], Vineal Sunkara [UG], Divya Krishna [HS], Siddarth Malhotra [HS]

Advisors: Ivan Seskar and Jennifer Shane

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Project Description & Goals:
Build and train miniature autonomous cars to drive in a miniature city.
RASCAL (Robotic Autonomous Scale Car for Adaptive Learning): Using the car sensors, offload image and control data onto a server node. This node will use a neural network that will train the vehicle to move around on its own given the image data it sees through its camera.

Technologies: ROS (Robot Operating System), Pytorch

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Week 1:

​Week 1 Slides

Progress:

• Familiarize with past summer's work:​GitLab, RASCAL setup, ​Software Architecture
• Debug issue with RASCAL's pure pursuit

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Week 2:

​Week 2 Slides

Progress:

• Setup X11 forwarding for GUI applications through SSH
• Visual odometry using Realsense Camera and ​rtabmap
• Streamline data pipeline that processes bag data (car camera + control data) into .mp4 video
• Detect ARUCO markers from a given image using Python & OpenCV libraries
• Setup Intersection server (node with GPU)
• Develop PyTorch MNIST model
• Trained "yellow thing" neural network
• Line up perspective drawing with camera to determine FOV

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Week 3:

[Week 3 Slides]

Progress:

• Created web display assassin to eliminate web server when closing ROS
• Tested "yellow thing" model, great results
• SSHFS setup
• Calibrate Real Sense camera
• Created "snap picture" button on web display for convenience
• Developed python script to detect ARUCO marker and estimate camera position

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