Our Simulation Team leads, Sarah Xu and Yijie Wang, were featured on the MathWorks blog! 💻 In their article, Sarah and Yijie explain how aUToronto’s Simulation team applies MathWorks tools to simulate V2X messaging and test behavior when yielding to an emergency vehicle.
Check out the blog here!
We're back with another video in colloboration with NoMachine! Find out how our team is effectively using NoMachine in our day-to-day operations with our Team Principal, Chad!
We are proud to present our new paper aUToPath: Unified Planning and Control for Autonomous Vehicles in Urban Environment Using Hybrid Lattice and Free-Space Search by Tanmay Patel, Connor Wilson, Ellina Zhang, Morgan Tran, Chad Paik, Steven L. Waslander, Timothy Barfoot at CRV 2025.
This paper presents aUToPath, a unified online framework for global path-planning and control to address the challenge of autonomous navigation in cluttered urban environments.
Check out our paper here!
A massive shoutout to Swit for transforming the way we work! From streamlining workflows to enhancing communication, Swit has truly elevated our productivity to the next level.
Join us as we take you behind the scenes and showcase how we’re using Swit in innovative ways to boost teamwork and efficiency like never before!
Big shoutout to NoMachine, our go-to solution for remote desktop access! Thanks to their powerful software, our team can seamlessly connect to our work computers from anywhere! 💻✨
Take a look at our crew how our team uses NoMachine in our day-to-day work! NoMachine has us covered whether we’re out testing or working from home as we gear up for our competition next June!
We would like to thank Element14 for sponsoring us with essential hardware our team used to develop our traffic lights! Watch Michael Acquaviva, our Electrical Subsystem Lead, speak more on how Element14’s sponsorship has positively impacted us this year:
Our Lane/Light team has made pivotal innovations in Traffic Light Detection and Lane Detection systems. We are developing advanced algorithms to optimize our system’s decision-making processes, achieving greater precision and timeliness in scenarios critical for autonomous navigation. Addressing real-world complexities, we’ve set robust goals for this year, focusing on system reliability, advanced localization, and comprehensive scenario testing.
We are exploring and integrating advancements from the broader autonomous driving sector, adopting techniques that enhance real-time object detection and association. Our detection systems are being refined, focusing on challenging driving scenarios and ensuring seamless integration with broader vehicular systems.
Our roadmap is strategic and ambitious, designed to achieve substantial progress within a well-defined timeline. Key future focuses include refining detection capabilities, optimizing system performance, and achieving seamless technology integration.
Gal Cohen - Lane/Light Team Lead
