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Paper TuA203.5

Dima, Cristian (Carnegie Mellon University), Wellington, Carl (NREC), Moorehead, Stewart (John Deere), Lister, Levi (Carnegie Mellon University), Campoy, Joan (Carnegie Mellon University), Vallespi-Gonzalez, Carlos (CMU), Jung, Boyoon (NavCom Technology, Inc.), Kise, Michio (John Deere), Bonefas, Zach (John Deere)

PVS: A System for Large Scale Outdoor Perception Performance Evaluation

Scheduled for presentation during the Regular Sessions "Autonomous Navigation II" (TuA203), Tuesday, May 10, 2011, 11:05−11:20, Room 3D

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on March 30, 2020

Keywords Autonomous Navigation, Field Robots, Learning and Adaptive Systems

Abstract

This paper describes the motivation, design and implementation of a Perception Validation System (PVS), a system for measuring the outdoor perception performance of an autonomous vehicle. The PVS relies on using large amounts of real world data and ground truth information to quantify performance aspects such as the rate of false positive or false negative detections of an obstacle detection system. Our system relies on a relational database infrastructure to achieve a high degree of flexibility in the type of analyses it can support.

We discuss the main steps required for going from raw data to numerical estimates describing the performance of the perception system, including the generation of ground truth information and the safe speed metric we found to be most useful for comparing the perception systemís outputs to the ground truth data.

We present results illustrating some of the analyses that can be completed using the Perception Validation System.

 

 

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