{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T05:52:05Z","timestamp":1791525125011,"version":"4.3.4"},"reference-count":47,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T00:00:00Z","timestamp":1791504000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurorobot."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Highway lane-change maneuvers pose a safety challenge for SAE Level 3 autonomous driving because conventional threshold-based decisions and isolated surrogate safety measures may inadequately capture the coupling between longitudinal braking demand and evolving inter-vehicle gaps. This study develops an interpretable, risk-aware framework for continuous lane-change assessment during maneuver initiation and execution.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>An adaptive neuro-fuzzy inference system (ANFIS) combines normalized longitudinal braking demand and minimum inter-vehicle gap to generate a continuous risk index ranging from zero to one. The index is integrated into a two-phase decision mechanism incorporating multi-vehicle threat assessment, region-dependent maneuver cancellation, quintic B\u00e9zier trajectory replanning, and driver takeover supervision. Performance was evaluated through high-fidelity SCANeR\/ReScan and MATLAB\/Simulink co-simulations against static Mamdani fuzzy, time-to-collision (TTC), deceleration-demand, and time-headway baselines. Robustness was assessed using 1,000 Monte Carlo simulations with variations in vehicle speeds, sensor noise, road friction, and cut-in timing.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Compared with the TTC baseline, the proposed framework increased warning lead time from 0.86 to 1.84 s and minimum two-dimensional clearance from 2.08 to 4.82 m, while reducing maximum deceleration demand from 4.92 to 2.34\n                      <jats:italic>m<\/jats:italic>\n                      \/\n                      <jats:italic>s<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      . Mean computational execution time was 1.42 ms, below the 10-ms simulation control-cycle budget. Across the 1,000 randomized simulations, no collisions were observed with the proposed framework; near-miss and false-alarm rates were 0.30% and 1.20%, respectively.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The simulation findings support the potential of continuous neuro-fuzzy risk assessment to provide earlier hazard detection and larger safety margins while retaining rule-based interpretability and low computational cost. Hardware-in-the-loop and real-vehicle testing remain necessary to establish performance beyond the evaluated simulation conditions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnbot.2026.1918469","type":"journal-article","created":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T05:34:30Z","timestamp":1791524070000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Adaptive neuro-fuzzy risk index for safe and interpretable lane-change decisions in Level-3 autonomous driving"],"prefix":"10.3389","volume":"20","author":[{"given":"Quanda","family":"Zu","sequence":"first","affiliation":[{"name":"Wenzhou Polytechnic University","place":["Wenzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaobo","family":"Cai","sequence":"additional","affiliation":[{"name":"Wenzhou Polytechnic University","place":["Wenzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianwei","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Advanced Manufacturing and Modern Equipment Technology, Jiangsu University","place":["Zhenjiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,10,9]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"138061","DOI":"10.1109\/ACCESS.2025.3593257","article-title":"A review of resilience enhancement techniques for cyber-physical systems","volume":"13","author":"Alain","year":"2025","journal-title":"IEEE Access"},{"key":"B2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1057\/s41599-022-01286-2","article-title":"How should autonomous vehicles drive? 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