Work Done

This week I finalized the lane detection in SUMO+TraCI.

I spent a whole day trying to debug why the function to collect the waiting time for a car did not work and realized it was ultimately due to a type error. There was no documentation on the error and the error did not indicate what was wrong, so I looked on forums and StackOverflow for guidance. Since SUMO is not a very widely used software, I did not find anything helpful to my error and had to just randomly try debugging and looking at the source code.

This is the error I was receiving for reference:

traci.exceptions.FatalTraCIError: Received answer 189,18,0 for command 173,18,0.

Also the software kept bugging out when I was placing the lane areas, so it required a lot of manual adjusting. It didn’t tell me what was wrong, but I guessed that it was due to a weird overlap error, so I had to move the lanes a bunch even though sometimes it worked and sometimes it didn’t.

This is a picture of the simulation running with lane area detectors that simulate what the cameras we have would detect. Since Ankita predicts about 50m detection on each side of the intersection, I set the lane area to be about 50 meters. The output shown on the left in the terminal is the average wait time of all the lane areas.

I am also finishing up the code to spawn in the cars based on the Traffic API data. The code is written, however I need to calibrate and map real life coordinates to the simulation coordinates and define the regions that represent the areas we plan on gathering API data from. I also plan on discussing with Ankita more about how I should implement the code to spawn in cars based on the camera data this week.

Schedule

I should be on track to finish the simulation spawning and an unoptimized machine learning model by the time we demo for the Interim Demo. I am posting this on Friday due to being busy all of Saturday, but I plan to finish the simulation spawning on Sunday/Monday to remain on track to have the basic ML model implemented next week.

I updated the schedule with the shifted tasks.

Tasks This Week

• Finish API to simulation calibrations
• Make a basic ML model implementing simulation – will optimize hyperparameters in upcoming weeks

Work Done

I finished working on functions to call the APIs and set everything up for both TomTom and HERE.

I also set up the SUMO simulation software on my laptop. This took really long because I was originally using my Macbook to install it, but I realized my Macbook doesn’t have administrator privileges so it can’t run some of the parts of SUMO, so I had to redo the setup on my Windows laptop. I then was able to make a simulation of the intersection near my apartment using the Open Street Map software SUMO comes included with. I am still working on how I can change the amount of cars at specific areas to simulate API data we get as well as figuring out TrACI, which is how we can import the data from the simulation into Python code.

Schedule

Everything seems to be on track for the most part. I do need to extend the SUMO integration task a little longer until Monday because there was a lot of trouble getting it set up on my Macbook originally and now I am using my Windows laptop to run it instead. This took a lot longer than expected when setting up and I realized there are a lot of different tools to play with in SUMO that I want to look through to make sure I am simulating everything well.

I also moved the API to optimization algorithm pipeline task to be later, since I don’t know exactly how I want the input until I get further in the ML algorithm.

Tasks This Week

• Finish SUMO integration with TrACI
• Continue working on optimization algorithm
• API to optimization algorithm pipeline