RoboRacer Autonomous Racing — Track 1

NTU DeepSpeed Recruitment Hackathon 26/27

Write the software that drives a 1/10-scale racing car around a circuit it has never seen, as fast as it can, without hitting anything. Everything you need — the simulator, the judging environment, reference algorithms and every official track — is in this repository. You will not need to clone anything else.

Submission deadline: 18 October 2026, 23:59 (SGT)

Quick start#

git clone --recurse-submodules https://github.com/NTUDeepSpeed/Recruitment_Hackathon_2627.git
cd Recruitment_Hackathon_2627
git checkout track1

./install/linuxmacoswindows/setup.sh          # or install/macos, or install/windows from WSL
./install/linuxmacoswindows/run.sh            # builds if needed, then drops you into the container

Then, inside the container:

cd /hackathon/race_ws && colcon build --symlink-install
source install/local_setup.bash

# Terminal 1 — the simulator
ros2 launch roboracer_referee simulator.launch.py

# Terminal 2 — your car
ros2 run team_driver driver

The car will set off and hit the first wall: the template is wiring, not a driver, and writing one is the hackathon. Once yours does something, score it:

./scripts/evaluate.sh --team your_team_name

Pushing also races your entry on GitHub Actions and writes the result to the workflow summary. That workflow is for reference only — it runs on GitHub's hardware and nothing it prints is scored. Your result is the organisers' run on the judging machine.

Full walkthrough: docs/01-setup.md.

Cloned without --recurse-submodules, or downloaded a ZIP? Run git submodule update --init --recursive. The setup scripts will also do it for you. A ZIP download will not work — submodules need a real clone.

The challenge#

You get a LiDAR scan and the car's exact position. You publish a steering angle and a speed. That is the whole interface.

No driver ships with this repository. team_driver is wiring — it talks to the simulator and drives in a straight line into the first wall. Writing something that laps is the hackathon. Reactive algorithms like follow-the-gap will get you round; planning against the map and your own position is where the lap time is; and replacing the approach outright is encouraged: reinforcement learning, MPC, imitation learning, a learned end-to-end policy, anything you can defend. The environment is ROS 2 Jazzy on Python 3.12 with a GPU available, so a learned policy is a realistic option. docs/04-algorithms.md is the menu, with what each approach needs and where each breaks.

Where things are#

Recruitment_Hackathon_2627/
├── install/                    Setup scripts, one folder per platform
│   ├── linux/  macos/  windows/    setup.sh, run.sh, shell.sh, stop.sh
│   └── common.sh
├── race_ws/src/                The ROS 2 workspace, mounted into the container
│   ├── team_driver/            ★ YOUR CODE GOES HERE — the template does not drive
│   └── roboracer_referee/        The judging environment — do not modify
├── scripts/                    evaluate.sh, leaderboard.py, track_tool.py, …
├── maps/                       The circuit: icra26.pgm, its yaml, tracks.yaml
├── docker/                     Image definition (ROS 2 Jazzy) and compose files
├── external/                   Upstream simulator sources, as git submodules
├── results/                    Where your run results land
├── .github/workflows/          Automated judging on every push
├── docs/                       This guide
└── workshop/                   ROS 2 teaching material from the workshop

The one file you are meant to open first: race_ws/src/team_driver/team_driver/driver.py

The guide#

Rules at a glance#

The full rules are in docs/06-rules.md and they are what counts. The short version:

• Teams of 3 to 5.
• Deadline: 18 October 2026, 23:59 SGT. Late entries are not scored.
• AI assistants are allowed. You will be asked to explain your code at the interview — generally, not line by line — so do not submit anything you cannot defend. A learned policy is held to the same standard, and meets it the same way: explain how you trained it and why, not what each weight means.
• Do not modify the judging environment. Check yourself with ./scripts/verify_judging_env.sh.
• Score (out of 100):

One warm-up lap is granted before timing starts. Each collision adds 10 s to the lap it happened on. More than 10 collisions in a run is a disqualification.

• Bonus marks at the interview, for work you can explain properly: replacing the ground-truth odometry with your own localisation; generating a racing line from the map at runtime; or an ambitious driving algorithm — reinforcement learning, MPC, imitation learning.

Getting help#

• Check the troubleshooting section at the end of docs/01-setup.md first — most problems are there.
• Bring the exact error text and what you ran to the team channel.
• ./scripts/verify_judging_env.sh will tell you if your environment has drifted from the official one.
• You may email ntu-deepspeed@e.ntu.edu.sg for further inquiries if you cannot solve the issues after troubleshooting.

Good luck. Go fast.