Where Intelligence Meets Mobility
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Autonomous Urban Adventure
This competition unfolds in two exciting stages — a virtual challenge and a live showdown — as teams take on the Autonomous Urban Adventure:
- Navigate the city to complete pick-up and drop-off requests.
- Make intelligent decisions to follow road rules, optimize routes, and maximize your “earnings.”
- Adapt in real time as the clock ticks down and your AI competes to climb the leaderboard.
February 27, 2026: Registration Closed
March 31, 2026: Video Submission Deadline
May.11 – May 14, 2026: In-person Competition
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1Stage 0: Registration
Team registration has opened, until February 27, 2026
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2Stage 1: Virtual Competition
Using Quanser QLabs Virtual QCar, teams will develop their control and perception algorithms entirely in MATLAB and Simulink, leveraging all the necessary toolboxes.
Teams will submit a demonstration video and a technical summary report detailing their design, methodology, and performance.
A panel of experts from MathWorks and Quanser will evaluate submissions to determine the top-performing teams.
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3Stage 2: On-Site Finals at CPS-IoT Week 2026
The top teams will earn their spot in Saint-Malo, France, for an unforgettable 4–day live competition during CPS-IoT Week 2026. They’ll bring their virtual designs to life on the physical Quanser QCar 2 platform.
For details, please visit the GitHub link in the Resources & Contact Tab.
First Place:
- $1000
- 1-Year License for MATLAB/Simulink and QLabs
- Certificates
Second and third Place:
- 1-Year License for MATLAB/Simulink and QLabs
- Certificates
GitHub: CPS IoT Competition Details
Important Links
- Apply for MathWorks Student Competition License: Click to download
- Get the Quanser Interactive Labs for MATLAB addon: Quanser Interactive Labs for MATLAB – File Exchange – MATLAB Central
- Learning Path: ADAS Learning resources for Students
How – to Videos
- How to Parse and Plot Sensor Data in MATLAB
- How to Create Custom Scenes in RoadRunner and Co-simulate with Simulink and Unreal Engine
- How to use Sensor Fusion and Multi Object Tracker
Getting Started
Tutorial Series
Contact us: racinglounge@mathworks.com
