SURF 2024: Task-Relevant Metrics for Perception: Difference between revisions

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(Created page with "'''2024 SURF Task-Relevant Metrics for Perception''' * Mentor: Richard Murray * Co-mentor: Apurva Badithela ==Project Description== right|800px|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 tr...")
 
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Goals for this SURF include:
Goals for this SURF include:
1. Proposing new metrics for tracking or other perception tasks, and rigorously connecting these metrics to system-level evaluations of safety.
* Proposing new metrics for tracking or other perception tasks, and rigorously connecting these metrics to system-level evaluations of safety.
2. Evaluating state-of-the-art perception models on the nuScenes dataset with respect to tracking metrics derived from system-level specifications
* Evaluating state-of-the-art perception models on the nuScenes dataset with respect to tracking metrics derived from system-level specifications
3. Time permitting, to validate theoretical results on a hardware platform such as Duckietown.
* Time permitting, to validate theoretical results on a hardware platform such as Duckietown.


==Desired:==
==Desired:==

Revision as of 22:51, 15 December 2023

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:

[1]

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