Virtual Lap Coach

Inspiration

Motorsport drivers and teams spend countless hours improving lap times, but precise lap-by-lap video analysis and feedback tools are either costly or unavailable for grassroots and sim racers. Simple mistakes like missing an apex, braking too late, or losing grip can go unnoticed without expert review. The need for affordable, actionable, real-time feedback for every driver inspired this project. If elite F1 teams use advanced analytics, why shouldn’t everyone have access to similar insight?

What it does ?

Virtual Qualifying Lap Coach is an always-on-top assistant that takes your inputs (track, weather, tire compound, etc.), then analyses your race or practice lap video. It provides instant, actionable feedback:

Highlights strong and weak sectors.

Detects mistakes (missed apex, grip loss, poor exits).
Displays sector by sector analysis and custom tips.
All feedback is presented as overlays while watching your video, or in a summary dashboard.

How we built it

Frontend: Built a desktop overlay (using Electron) that lets users enter their race conditions and loads their onboard/practice footage.
Video Analysis: Used Python and OpenCV to process video, tracking car position, estimating speed, and segmenting track areas using template matching.
Mistake Detection: Applied simple heuristics (like detecting wide lines, abrupt speed loss, and tire screech frames) to flag lapses in performance.
Feedback Engine: Generated tips by mapping detected events to actionable advice, contextualized for the user’s entered weather and tire data.
Report Generation: Exported lap analysis summaries as both on-screen overlays and shareable markdown reports.

Challenges we ran into

Accurate track and sector recognition when video angles varied.
Dealing with variable lighting and differing onboard camera qualities.
Ensuring overlay windows always stayed prominent and responsive.
Making feedback both accurate and understandable for all experience levels.

Accomplishments that we're proud of

This project empowers every driver to see exactly where to improve lap after lap, under any conditions

What we learned

OpenCV can deliver robust video insights even without complex machine learning.
Mapping video data to track maps and weather details helped us deliver personalized and relevant advice.
Getting simple user interfaces right can make advanced tools much more accessible.

What's next for Virtual Lap Coach

The next steps for our Virtual Lap Coach project focus on making it easy to use , scalable, and more valuable for race drivers and teams. we will be now more Focusing on accuracy, usability, platform reach, and actionable advice will help Virtual Lap Coach evolve into a powerful tool for both amateur and professional motorsport communities.