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Self-driving and automated vehicles rely on a comprehensive understanding of their surroundings and one another to operate effectively. While the use of sensors may allow the vehicles to directly perceive their environments, there are instances where information remains hidden from a vehicle. To address this, vehicles can transmit information between each other, enabling over-the-horizon awareness. We create a Robot Operating System simulation of vehicle-to-everything communication. Then, using two real-life electric vehicles equipped with global positioning systems and cameras, we aggregate time, position, and navigation information into a central database on a roadside unit. Our model uses an image classification deep learning model to detect obstacles on the road. Next, we create a web-based graphical user interface that automatically updates to display the vehicles and obstacles from the database. Finally, we use an occupancy grid to predict vehicle trajectories and prevent potential collisions. Our deep learning model has a precision-recall score of 0.995 and our system works across many devices. In the future, we aim to recognize a broader range of objects, including pedestrians, and use multiple roadside units to widen the scope of the model. 

Reliable lane-following is one of the most important tasks for an automated vehicle or ADAS. The intent of this project was to design and evaluate multiple lane-following algorithms for an automated vehicle using computer vision. The implemented algorithms' performance was then evaluated on a testing course and compared with a human driver. ROS and OpenCV were used to detect and follow lanes on the road. A street-legal vehicle with a high-definition camera and drive-by-wire system was used to implement and evaluate driving data. Each algorithm was evaluated based on time for completion, speed limit infractions, and lane positioning infractions. The recorded evaluation data determined the most reliable lane-following algorithm. All of our algorithms had a success rate of at least 60% on certain lanes of the testing course.