Winning team – Northeastern University
Every team demonstrated some aspects of self-driving and map generation with Purdue University and Northeastern having an impressive presentation on their approach to this challenge. The judging panel was comprised of Craig Buhr from MathWorks, Jonathan How from MIT, Paul Karam our Chief Operating Officer and myself, from the Academic Applications team at Quanser. While it came to a split decision the winning team was Northeastern University with an impressive display of object detection utilizing a YOLOV4 object detector, Occupancy Grid map representation, and key object identification and data summary (Link to their work: Self-Driving Car Competition – YouTube )
An Idea Birthed at ACC
Quanser has been a part of the American Controls Conference (ACC) for years. If you’ve had a chance to go to ACC and you ask about Quanser one familiar response you’ll hear is “Oh yeah they are the pendulum guys!”. This quote refers to one of the core products in Quanser’s list of controls related devices, the Qube Servo 2. In its’ 30+ year history Quanser has had the vision to develop and introduce, to universities all over the world, a set of highly complex and feature complete solutions. With this line of thinking and introduced in 2020, the Quanser QCar has given researchers the ability to dream about taking the complexities and nuances of real world driving and bring them into an indoor university setting. What’ been the catch so far? How does Quanser (As a group of research minded design engineers) help bring this vision into reality? This takes us into ACC 2022.
In an effort to articulate all the research capabilities available to professors in an easy to conceptualize application, our Chief Operating Officer Paul Karam architected a 3-vehicle driving demonstration. Combining aspects of self-localization, a centralized infrastructure, artificial intelligence for sign recognition and the use of sounds as audible cues, Quanser brought to ACC an idea of how interconnected systems (which resemble the real world) could be brought into a university lab. The demonstration would also spark the interest of the ACC committee for 2023 which were interested in bringing the QCar back and potentially do a more interactive/engaging approach to self-driving applications.
The ACC Challenge
These two ideas became the cornerstone of the ACC competition. Students were put in the perspective of an autonomous driving engineer working for a big company within the self-driving space. The ask for each team was to perform the following tasks:
- Could they develop a map which defines the world the car is driving in.
- Could they identify key objects in the environment and place them on the world map.
- Could they demonstrate aspects of self-driving while generating this world map.
- Summarize the findings of the map in a readable form.
FAQs
We suggest having a supervising professor on the competition team who may help coordinate resources and orchestrate progress.
We do not have a specific limit on the number of people on each participating team, and team composition is based on the best combination that will accomplish the task. Based on the 2023 competition’s experience, a team has an average of 5 members.
Yes, we will email the supervising professor to follow up.
First, we strongly recommend nominating a Team Captain, and we will contact your Team Captain via email. Secondly, please connect with us through LinkedIn, where you can find the most updated information on the competition.
Please email your specific requirement to studentcompetition@quanser.com. A dedicated Quanser engineer team will review and reply.
Registration and participation in the competition are complimentary. Teams advancing beyond Stage 1 will be responsible for their travel expenses and ACC conference registration during Stage 3 on-site competition in Toronto.
We understand that some team members may have visa issues to travel to Toronto. A possible solution is to let some team members join Stage 3 remotely while others go to the live event in Toronto.