{"type":"ScholarlyArticle","title":"Modeling Perception Errors towards Robust Decision Making in Autonomous Vehicles","authors":[{"name":"Andrea Piazzoni","slug":"andrea-piazzoni","given":"Andrea","family":"Piazzoni","affiliation":"ERI@N , Interdisciplinary Graduate School , Nanyang Technological University , Singapore"},{"name":"Jim Cherian","slug":"jim-cherian","given":"Jim","family":"Cherian","affiliation":"Centre of Excellence for Testing & Research of AVs , Nanyang Technological University , Singapore"},{"name":"Martin Slavik","slug":"martin-slavik","given":"Martin","family":"Slavik","affiliation":"Centre of Excellence for Testing & Research of AVs , Nanyang Technological University , Singapore"},{"name":"Justin Dauwels","slug":"justin-dauwels","given":"Justin","family":"Dauwels","affiliation":"Centre of Excellence for Testing & Research of AVs , Nanyang Technological University , Singapore; School of Electrical and Electronic Engineering , Nanyang Technological University , Singapore"}],"abstract":"Sensing and Perception (S&P) is a crucial component of an autonomous system (such as a robot), especially when deployed in highly dynamic environments where it is required to react to unexpected situations. This is particularly true in case of Autonomous Vehicles (AVs) driving on public roads. However, the current evaluation metrics for perception algorithms are typically designed to measure their accuracy per se and do not account for their impact on the decision making subsystem(s). This limitation does not help developers and third party evaluators to answer a critical question: is the performance of a perception subsystem sufficient for the decision making subsystem to make robust, safe decisions? In this paper, we propose a simulation-based methodology towards answering this question. At the same time, we show how to analyze the impact of different kinds of sensing and perception errors on the behavior of the autonomous system.","doi":"10.24963/ijcai.2020/483","url":"https://ijcai.org/proceedings/2020/483","pdf":"https://ijcai.org/proceedings/2020/0483.pdf","markdown":"https://ijcai.org/proceedings/2020/483.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/483","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Multidisciplinary Topics and Applications","track":"Main track","number":483,"pages":{"first":3494,"last":3500},"keywords":["Multidisciplinary Topics and Applications: Validation and Verification","Multidisciplinary Topics and Applications: Transportation","Robotics: Vision and Perception","Agent-based and Multi-agent Systems: Agent-Based Simulation and Emergence"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24418/file_24418.m3u8","length_sec":287,"provider":"hls"}],"references":[{"position":1,"raw":"[Amodei et al., 2016] Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané. 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