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Sci."],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Autonomous driving technology has made significant advancements in recent years, yet challenges remain in ensuring safe and comfortable interactions with human-driven vehicles (HDVs), particularly during lane-changing maneuvers. This paper proposes an improved double quintic polynomial approach for safe and efficient lane-changing in mixed traffic environments. The proposed method integrates a time-to-collision (TTC) based evaluation mechanism directly into the trajectory optimization process, ensuring that the ego vehicle proactively maintains a safe gap from surrounding HDVs throughout the maneuver. The framework comprises state estimation for both the autonomous vehicle (AV) and HDVs, trajectory generation using double quintic polynomials, real-time TTC computation, and adaptive trajectory evaluation. To the best of our knowledge, this is the first work to embed an analytic TTC penalty directly into the closed-form double-quintic polynomial solver, enabling real-time safety-aware trajectory generation without post-hoc validation. Extensive simulations conducted under diverse traffic scenarios demonstrate the safety, efficiency, and comfort of the proposed approach compared to conventional methods such as quintic polynomials, Bezier curves, and B-splines. The results highlight that the improved method not only avoids collisions but also ensures smooth transitions and adaptive decision-making in dynamic environments. This work bridges the gap between model-based and adaptive trajectory planning approaches, offering a stable solution for real-world autonomous driving applications.<\/jats:p>","DOI":"10.1007\/s44443-025-00300-2","type":"journal-article","created":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T10:45:31Z","timestamp":1766486731000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Safe and efficient lane-changing for autonomous vehicles: an improved double quintic polynomial approach with time-to-collision evaluation"],"prefix":"10.1007","volume":"38","author":[{"given":"Rui","family":"Bai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Teng","family":"Rui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiale","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4984-2183","authenticated-orcid":false,"given":"Hoi Leong","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi Wei","family":"Oung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fujiang","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,23]]},"reference":[{"key":"300_CR1","doi-asserted-by":"crossref","unstructured":"Chen Y-H, Liu S, Xiao W, Belta C, Otte M (2025) Control barrier functions via minkowski operations for safe navigation among polytopic sets. arXiv:2504.00364","DOI":"10.1109\/CDC57313.2025.11312188"},{"key":"300_CR2","unstructured":"Gong L, Liu J, Ma J, Liu L, Wang Y, Wang H (2024) Eadreg: Probabilistic correspondence generation with efficient autoregressive diffusion model for outdoor point cloud registration. arXiv:2411.15271"},{"key":"300_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.tra.2025.104525","volume":"199","author":"H Hao","year":"2025","unstructured":"Hao H, Yao E, Pan L, Chen R, Wang Y, Xiao H (2025) Exploring heterogeneous drivers and barriers in maas bundle subscriptions based on the willingness to shift to maas in one-trip scenarios. 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