{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T14:03:07Z","timestamp":1790431387012,"version":"4.1.0"},"reference-count":77,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:00:00Z","timestamp":1790035200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"National Science Foundation","award":["2317946"],"award-info":[{"award-number":["2317946"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Hum Factors"],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To identify driving maneuvers in remote driving under end-to-end latency and examine operator adaptation through within-session maneuver transitions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Remote driving systems enable operators to control highly automated vehicles when systems encounter operational limits. End-to-end latency disrupts adaptation and vehicle control, although operators may adjust driving behavior. Research on within-session adaptation clusters and maneuvers emerging under latency remains limited.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>Thirty-six participants completed nine remote driving simulator sessions, navigating construction zones under end-to-end latencies (0\u2013640\u00a0ms). Hidden Markov Models identified driving maneuvers from vehicle control data (speed, steering, throttle). Dynamic time warping and K-means clustering classified drives by maneuver-transition sequences. Chi-square tests and ANOVAs examined associations among adaptation clusters, participant-specific differences, experimental conditions, and human-factors measures, while participant-clustered factorial analysis examined maneuver composition.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Four maneuvers (adapted, transitional, overcompensation, and compensatory) and three adaptation clusters (No adaptation, Rapid adaptation, and Gradual adaptation) emerged. Adaptation clusters were associated with participant-specific differences rather than latency conditions or repeated trials. Rapid adaptation drives were associated with lower workload and higher trust than No adaptation drives and showed a trend toward increasing trust, whereas trust significantly declined in No adaptation cluster.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Remote driving operators exhibit adaptation clusters characterized by maneuver-transition sequences. Transitional maneuvers reveal intermediate stages. Cluster distributions were more strongly associated with participant-specific differences than tested experimental conditions, with adaptation patterns differing in trust and workload.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Application<\/jats:title>\n                    <jats:p>Training programs should assess adaptation capabilities and provide interventions rather than uniform protocols. Early cluster identification enables training on maneuver transitions that facilitate progression toward adaptation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/00187208261490983","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T13:49:52Z","timestamp":1790084992000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Modeling Driver Adaptation Patterns During Remote Driving"],"prefix":"10.1177","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-8453-8284","authenticated-orcid":false,"given":"Xingjian","family":"Ma","sequence":"first","affiliation":[{"name":"University of Wisconsin-Madison"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7827-8828","authenticated-orcid":false,"given":"Anthony D.","family":"McDonald","sequence":"additional","affiliation":[{"name":"University of Wisconsin-Madison"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,9,22]]},"reference":[{"key":"e_1_3_3_2_1","doi-asserted-by":"crossref","unstructured":"Agrawal R. 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