The programs written over the summer of 2021 while working for the University of Delaware's Information and Decision Sciences (IDS) Lab. For more information about the lab and its other projects, please visit https://sites.udel.edu/ids-lab/ . This repository and README will be updated somewhat reguarly as progress is made on these projects. Weird note: all commits and edits saved to this repo were made by myself, though they may be labeled as being committed by someone else because of two shared computers (a raspberry pi and a computer in the lab). I will be working on fixing this from time to time.

During the summer going into my senior year at UD, I created a set of programs to estimate the state of a car in the IDS Lab's Scaled Smart City (SSC) using only the vehicle's onboard sensors. This ensures the car would be able to follow a preset trajectory in the city if it were to lose contact with VICON (an effective GPS system) and/or the centralized control system (CCS). This was done by using an Extended Kalman Filter, and the code in this repository was used to advance the existing code for the SSC cars. Note: Most of the code for the cars in the SSC is in a private repository so the full programs will not be shown, though what code is shown in this repository (specifically in ./udssc_car-with_sensors) is the same as it is in the hidden repository (with the changes I've made added).

• udssc_car-with_sensors - the code that the SSC uses in its cars to run experiments, with a few small additions (listed in Arduino_Code_Addition.ino and Pi_Sensor_Script_Addition.py, below) to allow for sensor measurements, relays, and recordings. The vast majority of this code I did not write, but the original is located in a private repo (that should eventually be made public) [code posted with permission of the owners]. For more information on what I did specifically, see the projects below.

• Arduino_Code_Addition.ino - A few lines of code to be added to the Arduino that performs the low level controls for the system so it would record data from the sensors in the car (from a Zumo 32U4) and send it to the Raspberry Pi that wirelessly connects with the CCS. This was used in ./udssc_car-with_sensors/V4Car/V4Car.ino .

• Pi_Sensor_Script_Addition.py - The additions to the code that already runs on the RasPi during an experiment. This extra code allows the Pi to accept and save data from the Arduino and VICON while also exporting it to other files that will be analyzed later to calculate the variances in each measurement. This code will later include the code to estimate the state of the system (and uncertainty) using the previously mentioned Extended Kalman Filter. This was used in ./udssc_car-with_sensors/scripts/path_controller.py .

• Variance_Calculator.py - The script used to calculate the uncertatainties (variances) of each sensor on-board the car. This uses the files output by the Pi_Sensor_Script_Addition.py program additions.

• kalman_filter_calculation_script.py - The script to actually estimate the state of the system using an Extended Kalman Filter as well as output the confidence in the estimate. This is, also, designed to be in another program, specifically the path_controller script at ./udssc_car-with_sensors/scripts/path_controller.py .