{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T07:08:24Z","timestamp":1782976104862,"version":"3.54.5"},"reference-count":37,"publisher":"SAGE Publications","issue":"2-3","license":[{"start":{"date-parts":[[2020,6,24]],"date-time":"2020-06-24T00:00:00Z","timestamp":1592956800000},"content-version":"vor","delay-in-days":366,"URL":"http:\/\/www.sagepub.com\/licence-information-for-chorus"}],"funder":[{"DOI":"10.13039\/100000181","name":"air force office of scientific research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000181","id-type":"DOI","asserted-by":"publisher"}]},{"name":"UC-Philippine-California Advanced Research Institute"},{"name":"SRC CONIX Center"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["The International Journal of Robotics Research"],"published-print":{"date-parts":[[2020,3]]},"abstract":"<jats:p>One of the most difficult challenges in robot motion planning is to account for the behavior of other moving agents, such as humans. Commonly, practitioners employ predictive models to reason about where other agents are going to move. Though there has been much recent work in building predictive models, no model is ever perfect: an agent can always move unexpectedly, in a way that is not predicted or not assigned sufficient probability. In such cases, the robot may plan trajectories that appear safe but, in fact, lead to collision. Rather than trust a model\u2019s predictions blindly, we propose that the robot should use the model\u2019s current predictive accuracy to inform the degree of confidence in its future predictions. This model confidence inference allows us to generate probabilistic motion predictions that exploit modeled structure when the structure successfully explains human motion, and degrade gracefully whenever the human moves unexpectedly. We accomplish this by maintaining a Bayesian belief over a single parameter that governs the variance of our human motion model. We couple this prediction algorithm with a recently proposed robust motion planner and controller to guide the construction of robot trajectories that are, to a good approximation, collision-free with a high, user-specified probability. We provide extensive analysis of the combined approach and its overall safety properties by establishing a connection to reachability analysis, and conclude with a hardware demonstration in which a small quadcopter operates safely in the same space as a human pedestrian.<\/jats:p>","DOI":"10.1177\/0278364919859436","type":"journal-article","created":{"date-parts":[[2019,6,24]],"date-time":"2019-06-24T05:33:13Z","timestamp":1561354393000},"page":"250-265","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":104,"title":["Confidence-aware motion prediction for real-time collision avoidance\n                    <sup>1<\/sup>"],"prefix":"10.1177","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5866-6441","authenticated-orcid":false,"given":"David","family":"Fridovich-Keil","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering and Computer Sciences,University of California, Berkeley, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea","family":"Bajcsy","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Sciences,University of California, Berkeley, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaime F","family":"Fisac","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Sciences,University of California, Berkeley, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sylvia L","family":"Herbert","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Sciences,University of California, Berkeley, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steven","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Sciences,University of California, Berkeley, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anca D","family":"Dragan","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Sciences,University of California, Berkeley, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Claire J","family":"Tomlin","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Sciences,University of California, Berkeley, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2019,6,24]]},"reference":[{"key":"bibr1-0278364919859436","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2014.6907265"},{"key":"bibr2-0278364919859436","doi-asserted-by":"publisher","DOI":"10.1007\/s10514-013-9334-3"},{"key":"bibr3-0278364919859436","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2015.7139219"},{"key":"bibr4-0278364919859436","doi-asserted-by":"crossref","unstructured":"Bajcsy A, Herbert SL, Fridovich-Keil D, et al. (2018) A scalable framework for real-time multi-robot, multi-human collision avoidance.\n                      arXiv preprint arXiv:1811.05929\n                      .","DOI":"10.1109\/ICRA.2019.8794457"},{"key":"bibr5-0278364919859436","volume":"29","author":"Baker CL","year":"2007","journal-title":"Proceedings of the Annual Meeting of the Cognitive Science 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