Weekly Status Reports

Zina’s Status Report for 3/16/24

Zina’s Status Report for 3/16/24

This week I was focused on doing the layout for our custom PCB that connects the ArduinoUNO to the 12 traffic-light-simulation LEDs via a Texas Instruments LED driver. This is my first time doing PCB layout myself, so it was a bit challenging at first 

Ankita’s Status Report for 3/16/24

Ankita’s Status Report for 3/16/24

Work Done I started tagging the positive and negative images needed to train the vehicle classifier from traffic camera footage — I’m still waiting on some footage from the Fifth and Craig intersection from Zina to add more images to the training dataset and then 

Team Status Report for 3/16/24

Team Status Report for 3/16/24

Potential Risks and Mitigation Strategies

The biggest thing we are uncertain about right now is whether or not the videos we are taking at the actual intersection we want to model (Fifth and Craig) will be sufficient to train the model. It could be challenging to position the GoPro camera at a high enough position to be able to see all of the cars on one side of the intersection. We are working on getting a good set of these videos to try using to train the CV object detection model, but it is certainly possible that it will simply be too hard to get the right angle. In that case, we will train the model using only videos found online, which is not ideal, but should suffice.

Changes to System Design

There haven’t been any changes to the design this week. Mostly we are just working on implementing the planned aspects right now, and we will further evaluate our design choices as we get closer to the interim demo.

Overall Takeaways and Progress

  • The simulation is almost set-up so that we can begin testing our optimization algorithms when the time comes
  • We managed to get the IP camera connected to the RPi, which is a good sign for our overall system integration
  • PCB layout is almost complete and we will have it ordered by this coming Tuesday
Kaitlyn’s Status Report for 3/16/24

Kaitlyn’s Status Report for 3/16/24

Work Done This week I spent a lot of time working on the Ethics Assignment and also finishing up the simulation code to use in our ML model. At the moment, I am able to remotely run a SUMO simulation in Python using TraCI. I 

Zina’s Status Report for 3/9/24

Zina’s Status Report for 3/9/24

Given that the Design Report was due this week, we had to lock in a lot of the details that we were uncertain about up until now. The process of writing the report was very helpful and made us think critically about the more challenging 

Team Status Report for 3/9/24

Team Status Report for 3/9/24

Potential Risks and Mitigation Strategies

Currently we are having challenges setting up the RPi to work with the CMU-Secure WiFi. In case that does not work out, we will instead use a mobile hotspot to provide internet access to our RPi to perform the necessary API calls and connect to our IP cameras. Other than that, there are not many other challenges that we have needed to plan around this week.

Changes to System Design

The biggest change we made this week was that we will no longer plan to use addressable LEDs to represent the traffic light mockup circuit. This type of LED draws too much current for us to use an Arduino to control them as planned. Instead, we will use 12 LEDs at the output of a Texas Instruments LED Driver module that can output a constant current given the 5V source voltage of the Arduino. These components will all be soldered to our custom PCB.

Overall Takeaways and Progress

  • The design report forced us to iron out a lot more details that we had been going back and forth on before
  • We are more clear on which object algorithms will be most helpful for our project (Haar Cascades vs YOLO)
  • Most of the hardware details are set now so we can begin ordering and testing everything

NEEDS OUR SOLUTION MEETS

GLOBAL FACTORS (Ankita)

Part A: … with consideration of global factors. Global factors are world-wide contexts and factors, rather than only local ones. They do not necessarily represent geographic concerns. Global factors do not need to concern every single person in the entire world. Rather, these factors affect people outside of Pittsburgh, or those who are not in an academic environment, or those who are not technologically savvy, etc.

The intended stakeholders of the Traffix system include local transportation authorities and other organizations that manage traffic through urban areas (and potentially other settings). As a result, depending on where it is implemented, it could affect people all over the U.S. (though, due to the fact that protected left turns are unsupported and the system is only designed for 4-way intersections, its application will be limited to areas that have these kinds of intersections. This may exclude certain states. For similar reasons, most other countries would probably be excluded as well.) For those living in areas that do have these kinds of intersections, though, there is no barrier to use for those who are not technologically savvy or not in an academic environment, as the system will simply replace the traditional traffic light system. Therefore, its boost in efficiency will be enjoyed by any commuter that uses these 4-way intersections.

CULTURAL FACTORS (Kaitlyn)

Part B: … with consideration of cultural factors. Cultural factors are encompass the set of beliefs, moral values, traditions, language, and laws (or rules of behavior) held in common by a nation, a community, or other defined group of people.

At the moment, our design uses the SUMO simulation system which accurately simulates traffic in many different countries (Germany, China). We believe that this global accuracy allows our system to be adaptable to many different cultures since they are all simulated by the software. However, we do anticipate that some cultures are more likely to disobey traffic rules, such as running red lights, which our system does not account for at the time. In the future, we anticipate being able to incorporate sensors for this scenarios if we make further advancements to Traffix. For example, research indicates that California, Arizona, and Colorado have the most red light running instances, so we anticipate Traffix performing worse in those scenarios because people are less predictable. However, we believe that in most cases people generally follow traffic signals especially when they are not in long periods of traffic and frustrated.

Additionally, some states or countries may have stricter laws for running red lights, so our system is more likely to work in those areas, since we assume in our calculations that people are following the laws and signals.

ENVIRONMENTAL FACTORS (Zina)

Part C: … with consideration of environmental factors. Environmental factors are concerned with the environment as it relates to living organisms and natural resources.

When an inefficient traffic light system leaves one or more sides of an intersection idling for an excessive amount of time at a red light, it not only wastes drivers’ time, it also wastes the fuel in their cars. Given the fact that tens of millions of cars drive on American roads every day, all of that wasted fuel really starts to add up. It’s difficult to find exact data on how much this inefficiency contributes to carbon emissions, but we can do some simple calculations to at least get an idea of the scale of this problem. Let’s consider a typical non-commercial vehicle such as an SUV. Here are the facts:

  • 1 gallon of gasoline creates ~9g of CO2 emissions
  • The average SUV consumes 0.013 gallons of fuel per minute when idling. 
  • There are approximately 100 million daily trips taken in SUVs 

Now, let’s assume that each day, all of these SUVs each idle for only a single minute more at standard traffic lights than they would at Traffix-optimized lights. That amounts to a total of 11,700 kg of CO2 emitted every day due to the inefficiency of existing traffic systems. Therefore, the environmental benefits of implementing our product are potentially massive.

Kaitlyn’s Status Report for 3/9/24

Kaitlyn’s Status Report for 3/9/24

Work Done I spent most of the week working on the Design Report and focused on the introduction, project management, and content relating to the optimization algorithm. There were actually a lot of details that we had to flesh out relating to the optimization algorithm 

Ankita’s Status Report for 3/9/24

Ankita’s Status Report for 3/9/24

Work Done Last week, I along with our group members worked on and completed our 12-page design report. I completed the design requirements, block diagrams, and summary, as well as the architectural, implementation, testing, and trade study descriptions of the camera interfacing with the Raspberry 

Zina’s Status Report 2/24/24

Zina’s Status Report 2/24/24

This ended up being a very busy week for me, so I got a bit behind on my scheduled deliverables. I did create a lot clips of traffic camera footage using live camera feeds online so that Ankita could begin testing the object detection code. I made sure to find footage taken by cameras at a variety of angles, so that we can test our algorithm in many different situations. Additionally, I did some research on PCB design software and wrote out a detailed set of rules for how the Arduino should control light timings.

For the next week, I will be focused on getting our custom PCB designed so that we can eventually connect the traffic light circuit (TLC) to the Raspberry Pi. It will take a couple weeks to print, which is why we want to get it ordered before spring break. After that, I will be breadboarding the mockup TLC with red, yellow, and green LEDs. That way, I can test out my Arduino code and ensure that there is minimal latency between desired and actual light changes. I will also order the addressable LEDs that we decided on.

Team Status Report for 2/24/24

Team Status Report for 2/24/24

Potential Risks and Mitigation Strategies While we are feeling more confident with the optimization algorithm and the SUMO simulation platform, our camera situation is still uncertain due to an inability to connect to CMU-SECURE. We’ll be ordering a BLE camera to test our vehicle detection