Autonomous Intersection Manager
Autonomous Intersection Manager
Autonomous Intersection Manager
Revolutionizing Traffic Management
In a world where automation is becoming increasingly prevalent, we set out to tackle a common problem - traffic congestion at intersections. Traditional traffic signals force vehicles to stop and wait, causing delays, increased carbon emissions, and frustration among commuters. Our goal was to create a simulation that eliminates collisions at intersections by allowing automated cars to seamlessly navigate without the need for stoplights or signals. In this blog post, we'll take you through our journey of building this innovative project during a hackathon.
Our project was inspired by a vision of an intersection where vehicles could pass through without stopping, all controlled by computers. We saw videos showcasing autonomous intersections with no human intervention, and we were captivated by the idea. However, despite our excitement, we found that this concept had been explored before but not fully realized. That's when we decided to take on the challenge ourselves.
We embarked on this project during a hackathon, a 24-hour coding marathon. With limited time, we needed to make smart decisions and prioritize our tasks. We chose to use Pygame for graphics, which allowed us to create a visually appealing 4-lane intersection with cars of various sizes. This was a crucial step in making our simulation realistic and engaging.
The most significant challenge we faced was designing an algorithm that could handle all possible interactions between the cars at the intersection. With multiple lanes, varying car sizes, and different scenarios to consider, it was a complex puzzle to solve. We needed to account for cases where cars needed to slow down, speed up, or change lanes to avoid collisions. Our algorithm had to be efficient and adaptable.
Our algorithm dynamically adjusted the velocity and acceleration of each car in real-time. We focused on creating a system that mimicked real-life scenarios as closely as possible. We experimented with different combinations of speeding up and slowing down, finding the right balance to ensure safe and efficient navigation of the intersection.
One of our proudest achievements was the level of realism we achieved in our simulation. Unlike some simulations that make simplifications for the sake of ease, we aimed for a high degree of accuracy. We managed the velocities, positions, and accelerations of all cars, mirroring how cars behave in the real world. This made our simulation applicable to real-life traffic scenarios.
While our project was a success within the hackathon's time constraints, we see great potential for its expansion. We plan to further develop our simulation to account for turns and to handle unexpected situations that can occur in real-life traffic. Our ultimate vision is to create a solution that could be implemented in the real world to revolutionize traffic management.
Our hackathon project was a testament to our passion for innovation and problem-solving. We created a simulation that showcased the potential of automated intersections, paving the way for a future with smoother traffic flow and reduced emissions. We invite you to explore our project on GitHub and stay tuned for updates as we continue to work towards our vision of a traffic revolution.
Created by Akash Pamal, Jack Blair, and Rahel Selemon HackTJ 2020 12/13/20 MIT License
Revolutionizing Traffic Management
In a world where automation is becoming increasingly prevalent, we set out to tackle a common problem - traffic congestion at intersections. Traditional traffic signals force vehicles to stop and wait, causing delays, increased carbon emissions, and frustration among commuters. Our goal was to create a simulation that eliminates collisions at intersections by allowing automated cars to seamlessly navigate without the need for stoplights or signals. In this blog post, we'll take you through our journey of building this innovative project during a hackathon.
Our project was inspired by a vision of an intersection where vehicles could pass through without stopping, all controlled by computers. We saw videos showcasing autonomous intersections with no human intervention, and we were captivated by the idea. However, despite our excitement, we found that this concept had been explored before but not fully realized. That's when we decided to take on the challenge ourselves.
We embarked on this project during a hackathon, a 24-hour coding marathon. With limited time, we needed to make smart decisions and prioritize our tasks. We chose to use Pygame for graphics, which allowed us to create a visually appealing 4-lane intersection with cars of various sizes. This was a crucial step in making our simulation realistic and engaging.
The most significant challenge we faced was designing an algorithm that could handle all possible interactions between the cars at the intersection. With multiple lanes, varying car sizes, and different scenarios to consider, it was a complex puzzle to solve. We needed to account for cases where cars needed to slow down, speed up, or change lanes to avoid collisions. Our algorithm had to be efficient and adaptable.
Our algorithm dynamically adjusted the velocity and acceleration of each car in real-time. We focused on creating a system that mimicked real-life scenarios as closely as possible. We experimented with different combinations of speeding up and slowing down, finding the right balance to ensure safe and efficient navigation of the intersection.
One of our proudest achievements was the level of realism we achieved in our simulation. Unlike some simulations that make simplifications for the sake of ease, we aimed for a high degree of accuracy. We managed the velocities, positions, and accelerations of all cars, mirroring how cars behave in the real world. This made our simulation applicable to real-life traffic scenarios.
While our project was a success within the hackathon's time constraints, we see great potential for its expansion. We plan to further develop our simulation to account for turns and to handle unexpected situations that can occur in real-life traffic. Our ultimate vision is to create a solution that could be implemented in the real world to revolutionize traffic management.
Our hackathon project was a testament to our passion for innovation and problem-solving. We created a simulation that showcased the potential of automated intersections, paving the way for a future with smoother traffic flow and reduced emissions. We invite you to explore our project on GitHub and stay tuned for updates as we continue to work towards our vision of a traffic revolution.
Created by Akash Pamal, Jack Blair, and Rahel Selemon HackTJ 2020 12/13/20 MIT License
Revolutionizing Traffic Management
In a world where automation is becoming increasingly prevalent, we set out to tackle a common problem - traffic congestion at intersections. Traditional traffic signals force vehicles to stop and wait, causing delays, increased carbon emissions, and frustration among commuters. Our goal was to create a simulation that eliminates collisions at intersections by allowing automated cars to seamlessly navigate without the need for stoplights or signals. In this blog post, we'll take you through our journey of building this innovative project during a hackathon.
Our project was inspired by a vision of an intersection where vehicles could pass through without stopping, all controlled by computers. We saw videos showcasing autonomous intersections with no human intervention, and we were captivated by the idea. However, despite our excitement, we found that this concept had been explored before but not fully realized. That's when we decided to take on the challenge ourselves.
We embarked on this project during a hackathon, a 24-hour coding marathon. With limited time, we needed to make smart decisions and prioritize our tasks. We chose to use Pygame for graphics, which allowed us to create a visually appealing 4-lane intersection with cars of various sizes. This was a crucial step in making our simulation realistic and engaging.
The most significant challenge we faced was designing an algorithm that could handle all possible interactions between the cars at the intersection. With multiple lanes, varying car sizes, and different scenarios to consider, it was a complex puzzle to solve. We needed to account for cases where cars needed to slow down, speed up, or change lanes to avoid collisions. Our algorithm had to be efficient and adaptable.
Our algorithm dynamically adjusted the velocity and acceleration of each car in real-time. We focused on creating a system that mimicked real-life scenarios as closely as possible. We experimented with different combinations of speeding up and slowing down, finding the right balance to ensure safe and efficient navigation of the intersection.
One of our proudest achievements was the level of realism we achieved in our simulation. Unlike some simulations that make simplifications for the sake of ease, we aimed for a high degree of accuracy. We managed the velocities, positions, and accelerations of all cars, mirroring how cars behave in the real world. This made our simulation applicable to real-life traffic scenarios.
While our project was a success within the hackathon's time constraints, we see great potential for its expansion. We plan to further develop our simulation to account for turns and to handle unexpected situations that can occur in real-life traffic. Our ultimate vision is to create a solution that could be implemented in the real world to revolutionize traffic management.
Our hackathon project was a testament to our passion for innovation and problem-solving. We created a simulation that showcased the potential of automated intersections, paving the way for a future with smoother traffic flow and reduced emissions. We invite you to explore our project on GitHub and stay tuned for updates as we continue to work towards our vision of a traffic revolution.
Created by Akash Pamal, Jack Blair, and Rahel Selemon HackTJ 2020 12/13/20 MIT License
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built with ❤️ by Jack Blair on a very late night
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