Who make drivers stop? Towards driver-centric risk assessment: Risk object identification via causal inference

Chengxi Li, Stanley H. Chan, Yi Ting Chen

研究成果: Conference contribution同行評審

摘要

A significant amount of people die in road accidents due to driver errors. To reduce fatalities, developing intelligent driving systems assisting drivers to identify potential risks is in an urgent need. Risky situations are generally defined based on collision prediction in the existing works. However, collision is only a source of potential risks, and a more generic definition is required. In this work, we propose a novel driver-centric definition of risk, i.e., objects influencing drivers' behavior are risky. A new task called risk object identification is introduced. We formulate the task as the cause-effect problem and present a novel two-stage risk object identification framework based on causal inference with the proposed object-level manipulable driving model. We demonstrate favorable performance on risk object identification compared with strong baselines on the Honda Research Institute Driving Dataset (HDD). Our framework achieves a substantial average performance boost over a strong baseline by 7.5%.

原文English
主出版物標題2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
發行者Institute of Electrical and Electronics Engineers Inc.
頁面10711-10718
頁數8
ISBN(電子)9781728162126
DOIs
出版狀態Published - 24 十月 2020
事件2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020 - Las Vegas, United States
持續時間: 24 十月 202024 一月 2021

出版系列

名字IEEE International Conference on Intelligent Robots and Systems
ISSN(列印)2153-0858
ISSN(電子)2153-0866

Conference

Conference2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020
國家United States
城市Las Vegas
期間24/10/2024/01/21

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