A team of University of Toronto engineering researchers are working to enhance the reasoning ability of robotic systems, such as autonomous vehicles, with the goal of increasing their reliability and safe operation in changing environments.
Multi-object tracking, a critical problem in self-driving cars, is a big focus at the Toronto Robotics and AI Laboratory led by Professor Steven Waslander (UTIAS). The process used by robotic systems tracks the position and motion of moving objects, including other vehicles, pedestrians and cyclists, to plan its own path in densely populated areas.
The tracking information is collected from computer vision sensors — 2D camera images and 3D LIDAR scans — and filtered at each time stamp, 10 times a 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 (UTIAS PhD student).
“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.”