{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T05:17:12Z","timestamp":1774243032642,"version":"3.50.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,8]]},"abstract":"<jats:p>Autonomous vehicle (AV) software is typically composed of a pipeline of individual components, linking sensor inputs to motor outputs. Erroneous component outputs propagate downstream, hence safe AV software must consider the ultimate effect of each component\u2019s errors. Further, improving safety alone is not sufficient. Passengers must also feel safe to trust and use AV systems. To address such concerns, we investigate three under-explored themes for AV research: safety, interpretability, and compliance. Safety can be improved by quantifying the uncertainties of component outputs and propagating them forward through the pipeline. Interpretability is concerned with explaining what the AV observes and why it makes the decisions it does, building reassurance with the passenger. Compliance refers to maintaining some control for the passenger. We discuss open challenges for research within these themes. We highlight the need for concrete evaluation metrics, propose example problems, and highlight possible solutions.<\/jats:p>","DOI":"10.24963\/ijcai.2017\/661","type":"proceedings-article","created":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T05:14:07Z","timestamp":1501218847000},"page":"4745-4753","source":"Crossref","is-referenced-by-count":188,"title":["Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning"],"prefix":"10.24963","author":[{"given":"Rowan","family":"McAllister","sequence":"first","affiliation":[{"name":"University of Cambridge"}]},{"given":"Yarin","family":"Gal","sequence":"additional","affiliation":[{"name":"University of Cambridge"},{"name":"Alan Turing Institute"}]},{"given":"Alex","family":"Kendall","sequence":"additional","affiliation":[{"name":"University of Cambridge"}]},{"given":"Mark","family":"van der Wilk","sequence":"additional","affiliation":[{"name":"University of Cambridge"}]},{"given":"Amar","family":"Shah","sequence":"additional","affiliation":[{"name":"University of Cambridge"}]},{"given":"Roberto","family":"Cipolla","sequence":"additional","affiliation":[{"name":"University of Cambridge"}]},{"given":"Adrian","family":"Weller","sequence":"additional","affiliation":[{"name":"University of Cambridge"},{"name":"Alan Turing Institute"}]}],"member":"10584","event":{"name":"Twenty-Sixth International Joint Conference on Artificial Intelligence","theme":"Artificial Intelligence","location":"Melbourne, Australia","acronym":"IJCAI-2017","number":"26","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)","University of Technology Sydney (UTS)","Australian Computer Society (ACS)"],"start":{"date-parts":[[2017,8,19]]},"end":{"date-parts":[[2017,8,26]]}},"container-title":["Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T07:55:00Z","timestamp":1501228500000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2017\/661"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2017,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2017\/661","relation":{},"subject":[],"published":{"date-parts":[[2017,8]]}}}