Researchers at the University of Toronto Institute for Aerospace Studies (UTIAS) have developed two innovative tools to improve the safety and reliability of self-driving cars. These tools enhance the reasoning abilities of robotic systems, helping them better track the position and movement of objects like vehicles, pedestrians, and cyclists in busy areas. The first tool, called Sliding Window Tracker (SWTrack), uses extra information over time to prevent missing objects and improve tracking accuracy. The second tool, Multi-object tracking, collects information from computer vision sensors to predict future movements of objects. The team tested their tools using data from nuScenes and found that longer temporal windows improved tracking performance.
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