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About SuperLap
SuperLap is an AI-powered racing line optimization system designed to help superbike riders improve their performance on the track. Built with accessibility in mind, the platform uses a combination of Reinforcement Learning and Computer Vision to analyze a top-down image of any racetrack and simulate thousands of potential paths. It then overlays the fastest possible racing line directly on the track image.
Whether you're a student, hobbyist, coach, or sim racing enthusiast, SuperLap delivers data-driven insights and visual feedback that were previously only available through expensive telemetry systems. Users can upload custom track images, compare AI-generated lines against their own, simulate laps with animated bike movement, and even receive simplified guidance like braking and turn-in points.
SuperLap supports 2D and 3D visual modes, makes room for experimentation with different AI models, and aims to enhance both real-world and virtual race training. It’s a powerful, cost-effective alternative for anyone looking to sharpen their racing strategy -- no special hardware or prior data required.
About Quintessential
As a third-year Computer Science student, I’ve developed a strong foundation in data handling, software engineering, and cybersecurity. My academic journey and hands-on experience have allowed me to build confidence in working with technologies like C++, Python, Git, Postman, Express, and Node.js. I'm particularly drawn to understanding how systems work under the hood and enjoy exploring ways to use code more effectively and meaningfully. One of the highlights of my studies so far was working on a group project where we built a basic city builder game. It was a rewarding experience that pushed us to think creatively, collaborate effectively, and apply what we’d learned in a practical setting. I take pride in writing clean, readable code and have a knack for debugging and testing— skills I believe are just as critical as development itself. I’m always looking for opportunities that challenge me and help me grow, and this project genuinely excites me. It aligns perfectly with my interests and offers a chance to dive deeper into areas I'm passionate about.
About Quintessential
I’m a final-year Computer Science student with a strong foundation in C++ and Java, with a particular preference for C++ due to its performance and versatility. Over the years, I’ve explored Unity, Unreal Engine, and Blender through self-taught projects, where I developed a deep appreciation for interactive, visual experiences and how code brings them to life. I’m especially fascinated by machine learning—watching systems adapt and evolve unique solutions feels like witnessing intelligence emerge. While I enjoy frontend and UI development for its visual creativity, I’m excited to deepen my understanding of AI and ML in this project. I've built small game AIs before and I’m eager to now contribute to a larger, more robust system. This project offers the perfect opportunity to collaborate in a team setting and build an industry-level AI solution that I'm proud of. I’m particularly drawn to the challenge of combining smart design with efficient problem-solving, and I look forward to helping bring our racing intelligence system to life.
About Quintessential
I’m a final-year BSc Information and Knowledge Systems student with strong skills in full-stack and backend development, and a passion for AI-driven solutions. I’ve contributed to a custom database management system and built various apps, games, and websites, blending technical and creative development. I'm most proficient in Java and C++, with additional experience in Python, React, Angular, .NET, Postman, Git, and Unity. I’m eager to expand my knowledge in areas like PyTorch and OpenCV. Though I don’t have formal industry experience, my role at the university library has honed my communication and teamwork skills. I’m especially excited to work on backend systems and AI integration for the SuperLap Racing Line Optimization System, focusing on reinforcement learning and track analysis. As a racing and F1 enthusiast, this project combines my interests and technical goals—particularly in machine learning, image processing, and cloud development—making it a dream opportunity.
About Quintessential
I am a final-year BSc Computer Science student. I’m a curious programmer, always looking for my next programming project to learn new things. I have experience with C++, Java, Python, Nodejs. I’m Excited to delve into AI and learn more about deep learning and image processing. I learn quickly, and excel at doing research on projects, and how to implement features efficiently. I developed the database system and business management software for Simulator Cave. Thus, I have experience with working with clients and delivering a quality product based off their expectations. On the SuperLap Racing Line Optimization System, I am excited to work with proper machine learning algorithms, and use image processing to gather data. I’m excited to see the improvements to the model and learn from each issue that might arise.
About Quintessential
I am a third-year Computer and Data Science student with academic experience in various programming languages, including C++, Java, JavaScript/TypeScript, and Python. In my personal time, I find myself working on projects mostly using Rust, Go, and Dart, whether that be for game development using bevy, backend work or frontend development with Flutter. Based on this background, I believe my skills in web development (JavaScript/TypeScript), Python, and my prior C# game development experience in particular are relevant to this project. During university recesses, I participate in voluntary vacation work at BBD Software. This role has provided invaluable experience in a fast-paced environment, significantly improving my communication and collaboration skills within both large and small teams. Furthermore, it has given me direct exposure to client interactions. I consider myself a fast learner and an effective problem-solver, ready for all challenges, and look forward to working on this project.
Frequently Asked Questions
Where can I find the system?
The system is currently under development and not yet available for download. We're working hard to finalize everything and ensure a smooth user experience. Please check back for updates — the full system will be available by September 29th, 2025.
Will I need any special hardware to use SuperLap?
No special hardware is required. SuperLap is designed to run on standard desktops or laptops. All you need is a clear top-down image of your racetrack. In the future, we may provide optional integrations for external telemetry devices.
What kind of data will the data need from me?
Initially, you’ll only need to upload a top-down image of the racetrack. No prior data is required. In future updates, the system will support optional user data inputs (such as lap times or bike settings) to further personalize and refine the racing line recommendations.
How accurate is the AI?
SuperLap uses reinforcement learning trained on thousands of simulations to produce highly optimized racing lines. While it offers strong guidance, real-world conditions like rider style, bike setup, and weather may slightly affect results. It's best used as a training aid, not an absolute rule.
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