Written by: Mitchell Fong
Modern traffic management has long relied on fixed schedules and predetermined cycles – systems that cannot adapt to the inherent variability of the road. A recent research paper developed by McMaster’s EcoCAR Connected & Automated Vehicle’s (CAVs) Co-Lead, Connor Ricotta, challenges that exact system directly. The research paper, Traffic Light Release Order Optimization For McMaster EcoCAR Automatic Intersection Navigation, discusses the new possibility of producing intelligent systems not from within the car but throughout the traffic environment.
From AIN to Optimization: The Research Foundation
To truly appreciate this paper, it is essential to understand the foundation that Automatic Intersection Navigation (AIN) is in autonomous vehicle research. AIN is a standalone system designed to help a car interact with intersections autonomously and is one of the core features being developed in the EcoCAR vehicle. The technology greatly relies on Vehicle-to-infrastructure (V2I) communication, allowing the car to receive live data from smart intersections, such as light state, vehicle position, and remaining time in the current light state. The process entails receiving signals from the smart intersection, assessing the environment, and making decisions without human aid. Essentially, current state AIN allows for a car to receive data from an intersection and make an intelligent decision based off live data.
Although seemingly efficient, the current AIN system focuses on only how one specific car will interact – each vehicle contains its own AIN system and reacts in accordance with its own car-based system. Mr. Ricotta’s research takes that foundation and extends it into a universal design. Rather than focusing on what a single vehicle does at the intersection, his research examines how the traffic control system itself can be optimized. Mr. Ricotta explains his perspective on the overall problem in the following quote: “AIN tells a vehicle how to respond to an intersection. This research flips that — it’s about designing an intersection that responds to its vehicles.”
Rethinking Traffic Control from the Ground Up
Currently, traditional lights operate on a predetermined schedule with set times on each light state. As you can imagine, the rigid structure causes unnecessary traffic. On the contrary, Connor’s research develops an algorithm that monitors several factors such as the number of vehicles in each lane, pedestrians, how many vehicles pass through the intersection and much more, all of which, are used to determine when to switch light states. The algorithm allows for a granular approach, one that treats each intersection uniquely, like its own system that must respond to its environment.
“Once you stop thinking about individual cars and start thinking about the intersection as a whole, a completely different set of solutions opens up.”
Why This Matters for EcoCAR and Beyond
Currently, the V2I standard that makes AIN possible is very much still in development. Physical infrastructure upgrades are needed to equip traffic lights with the software required to communicate, and widespread adoption remains to be seen. But research like Connor’s is exactly what brings technology like this to reality.
For the McMaster EcoCAR team, this research spotlight is more than just an academic achievement. It represents a shift towards an autonomous future, one that isn’t just about what happens inside the car but rather making transportation safer, more efficient and overall better. Mr. Ricotta’s research is more than just a technical breakthrough; it’s about making smarter systems all the way from the vehicle to the road and the environment that it travels through.
If you want to learn more about research similar to Mr. Ricotta’s, you can visit the McMaster Automative Research Centre’s publications here: https://electrification.mcmaster.ca/publications/
