PRADA (Post Race Assesment & Data Analysis)

A tool to improve the quality of life for race engineers by easily comparing 2 cars with a simple UI and visual results. It creates better post-race communication between engineers and drivers.

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Inspiration

Our team first heard about Hack The Track in our Vehicle Dynamics course at Auburn University. We are all seniors in Mechanical Engineering (May 2026 graduation) and are car & racing enthusiasts, data fanatics, looking for opportunities to combine classroom skills with hobbies and interests. We are all currently looking for full-time job opportunities within the Motorsport Realm!

What it does

Our PRADA Tool takes all the given data (throttle, braking, steering, speed, gear, and lap times) and uses it to derive position, fuel efficiency, and sector times. It creates a position plot showing the two cars driving their fastest laps, while overlaying steering, brake, and throttle inputs. It also generates several additional plots comparing braking, speed, throttle, steering, and fuel efficiency between the two selected cars. All the user has to do is choose the two cars to compare and hit run. A dynamic UI featuring live animations, driver coaching, optimal lap times, and section-by-section deltas, along with fuel and lap time comparisons for laps in traffic or open air.

How we built it

We built this program almost entirely in MATLAB. All data was initially accessed through Microsoft Excel. The tool combines multiple user-defined functions that filter the needed data and use given parameters to derive the ones that aren’t provided, such as position and fuel efficiency. It then plots everything in an easy-to-understand visual format. Noise and potential aliasing in the data were filtered through skills learned in our Controls and Vehicle Dynamics courses taught at Auburn University

Challenges we ran into

Cleaning and filtering the data was one of the first major challenges, especially because some files were very large and difficult to work with. Implementing our ideas in MATLAB was also a challenge; we all had initial concepts but little experience applying them. The overall scale of the project was much larger than anything we had dealt with before, but through willpower, creativity, and experience, we powered through!

Accomplishments that we're proud of

Creating the UI was a major accomplishment, as it was a first for all of us. We’re also proud of our data-verification skills; when plots were incorrect, we consistently recognized the issues and traced them back to their sources. We were able to verify data by using past experience with race data handling through the Racing Simulation Data collected through the Auburn iRacing Team and personal sim driving. MoTec & PI Toolbox were big inspirations for our project. With this many lines of code, errors are inevitable, and being able to troubleshoot effectively was essential.

What we learned

We learned just how large the data scale is for a race team like this and what it’s like to actually work with such datasets. We also became significantly more proficient with MATLAB and data analysis as a whole. Learning how to problem solve, think, and communicate are the biggest skills for an engineer, and we built all of those skills through this project. We also learned how to identify noise and improper data more efficiently and how to troubleshoot those issues.

What's next for PRADA (Post Race Analysis Dashboard Accessory)

Our next goal is to make PRADA more versatile, using machine learning to create more optimal driving lines for the drivers based on their habits and more in-depth analysis involving PDF write-ups for drivers and engineers to read over and analyze when traveling between race weekends. We also want to create a standalone program with fewer required inputs to increase convenience for engineers, such as an app.

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