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Researchers at the University of Toronto Institute for Aerospace Studies (UTIAS) have introduced a pair of high-tech tools that could improve the safety and reliability of autonomous vehicles by enhancing the reasoning ability of their robotic systems.

The innovations address multi-object tracking, a process used by robotic systems to track the position and motion of objects—including vehicles, pedestrians and cyclists—to plan the path of self-driving cars in densely populated areas.

Tracking information is collected from computer vision sensors (2D camera images and 3D LIDAR scans) and filtered at each time stamp, 10 times per second, to predict the future movement of moving objects.

"Once processed, it allows the robot to develop some reasoning about its environment. For example, there is a human crossing the street at the intersection, or a cyclist changing lanes up ahead," says Sandro Papais, a Ph.D. student in UTIAS in the Faculty of Applied Science & Engineering. "At each time stamp, the robot's software tries to link the current detections with objects it saw in the past, but it can only go back so far in time."

In a new paper presented at the 2024 International Conference on Robotics and Automation in Yokohama, Japan, Papais and co-authors Robert Ren, a third-year engineering science student, and Professor Steven Waslander, director of UTIAS's Toronto Robotics and AI Laboratory, introduce Sliding Window Tracker (SWTrack)—a graph-based optimization method that uses additional temporal information to prevent missed objects.

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