SURF 2024: Task-Relevant Metrics for Perception

2024 SURF Task-Relevant Metrics for Perception

• Mentor: Richard Murray
• Co-mentor: Apurva Badithela

Project Description

Caption: System-level requirements are easier to formalize than requirements on perception tasks.

Problem

In this SURF, we will explore the interface between perception and planning more carefully. Misclassification or misdetection in a single frame is unlikely to trigger a different decision from the planner. Therefore, tracking objects across multiple frames needs to be accounted

Goals for this SURF include:

• Proposing new metrics for tracking or other perception tasks, and rigorously connecting these metrics to system-level evaluations of safety.
• Evaluating state-of-the-art perception models on the nuScenes dataset with respect to tracking metrics derived from system-level specifications
• Time permitting, to validate theoretical results on a hardware platform such as Duckietown.

Desired:

• Experience programming in Python, ROS, OpenCV.
• Coursework in control, robotics, computer vision.
• Interest in theoretical research, robotics, and working with hardware, and industry datasets such as nuScenes.

References:

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