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American Control Conference Self-Driving Car Student Competition

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Award Overview

Self-driving technology has become increasingly prevalent, driving a demand for research to validate safety and reliability. With countless unique scenarios faced daily by drivers, algorithms must identify situations and conclude correct behaviors. At their algorithmic core, self-driving cars excel or are held back in their capacity for autonomy by their knowledge and understanding of the state of the surrounding environment.

The ACC competition presents a great opportunity to showcase world-class driving algorithms running in a safe and approachable manner at a 1/10th scale and in a virtual setting. The entire competition is designed as a combination of virtual and on-site competitions, giving student teams the opportunity to fully engage with QCar and its digital twin, QLabs Virtual QCar 

While the details of the competition may vary from year to year, it has always been Quanser’s endeavour to perpetually support students’ continued progress and innovation in the field of Self-driving! 

Forty teams from 28 universities across 15 countries participated in three stages of learning, training, and practice to become self-driving car experts. At the American Control Conference 2024, Czech Technical University in Prague emerged as the champion of the 2024 Self-Driving Car Student Competition, held in Toronto, Canada!

The kick off of competition 2025 is around the corner, we are going to announce the challenge and rules soon, sign up here for the upcoming news! 

Past and On-going Competitions

2025

Location
Denver, Colorado, US
See you next year!

2024

Location
Toronto, Canada
Winning Team

Czech Technical University in Prague

Team Fast & Driverless
From more than 40 universities all over the world, Czech Technical University stood out and won the championship!

2023

Location
San Diego, United States
Winning Team
The competition focused on applying the Quanser QCar in both real-world and virtual environments. Among the four participating universities, the Northeastern University Team achieved the highest score, showcasing exceptional object detection using a YOLOV4 object detector, Occupancy Grid map representation, and proficient key object identification and data summarization.

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