This Github Repo will host all the competition resources you will need to successfuly complete the competition.
Everyone who has received notice that they are moving on to Stage 2 and 3 please switch your branch over to Stage2.
- A significant performance limitation was found in the QLabs interface to ROS, please check the following post out if you are receiving very low QLabs CPS performance: Why is My QLabs Performance Low?
- A patch to the libraries has been made that allows you to open the QCar 2 HIL Card in the virtual world. Please download the ACC resources and run
setup_linux.py. Please check out the Python Development for more details. - York University has kindly shared a progress video with everyone: York Progress. We hope it provides inspiration!
- It is important to read the Handbook, which lays out the rules of the competition in detail!
- There is a new Development Guide to help understand how to develop in this Docker environment.
- There is a new Detailed Scenario to help provide a target for your algorithms.
- There is a new FAQ to help address common issues.
- To get started with the competition please view the Software_Guides folder.
- We want to make it very clear that the intended use case is using Ubuntu 24.04, an Nvidia based graphics card, and ROS2 Humble via our Docker container for the competition. But all teams may use different softwares if they can get them to work. The Dev Team won't support the different softwares.
- Important updates have been made to the ACC_Software_Instructions. Please check them out to ensure any issues you are running into haven't been solved yet.
- If you have any questions not answered in the Discussions tab or the Issues tab, please email [email protected].
In this year’s competition, teams will try to maximize their profits as an autonomous taxi service. They will need to navigate through the city to selected pick-up and drop-off coordinates. During the trip, they will encounter different traffic scenarios that they will need to solve while adhering to the rules of the road. Depending on their performance during the trip, the teams will receive a rating for the executed ride that will influence the amount of money earned for the completion of the ride. Students will then receive additional ride requests and will try to maximize the amount of money they earn within a fixed timeframe. The three stages of the competition are outlined below:
Stage 1: Virtual Design and Submission
Stage 2: Implementation of Self-Driving algorithm on the Physical QCar 2
Stage 3: On-site Competition
All registrants will gain access to the Stage 1 virtual design phase, where a team’s skills will be tested in Quanser’s virtual environment. The top 6 teams, based on selected criteria, will be chosen to move on to the second and third stages of the competition. In stage 2, the selected teams will receive a physical QCar 2 from Quanser and implement their Self-Driving algorithms on the QCar2. They will then be required to bring their QCar 2 to the competition venue and compete live against other selected teams. More details will be released about stages 2 and 3 in a subsequent handbook.
Stage 1 will have a video submission and the criteria that will be considered is laid out in the Handbook.
To begin with the competition using ROS and Linux follow the Software_Guides.
This FAQ is for questions that require a more detailed response and might be relevant to everyone. Also check the open and closed issues to see if your specific problem has been addressed.
- How can I reset the entire setup of my resources?
- QCar 2 won't spawn in the open plane
- Hardware requirements and performance expectations
- What are my camera intrinsics and extrinsics?
- What are the transformation matrices between the different sensors?
- Why is My QLabs Performance Low?
Teams may discuss amongst each other in the discussions tab of the Github.
We would also like you to post any issues with supplied resources if you find them so that other teams may help or a Quanser representative can address them!
- FAQ
- Detailed Scenario
- Development Guide
- How to setup my Linux machine
- Handbook
- Competition Resources
- Password: acc2025denver
- Base Scenarios (Python)
- Competition Website
- Registration (Closed)
- Quanser Interactive Labs Support Page
- Quanser Interactive Labs Documentation
- Quanser Python API Documentation
- Quanser C API Documentation
- Quanser QUARC Blocks for MALTAB/Simulink Documentation