Velocity Prediction Based on Vehicle Lateral Risk Assessment and Traffic Flow: A Brief Review and Application Examples

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dc.authoridCoskun, Serdar/0000-0002-7080-0340
dc.contributor.authorLi, Lin
dc.contributor.authorCoskun, Serdar
dc.contributor.authorWang, Jiaze
dc.contributor.authorFan, Youming
dc.contributor.authorZhang, Fengqi
dc.contributor.authorLangari, Reza
dc.date.accessioned2025-03-17T12:25:16Z
dc.date.available2025-03-17T12:25:16Z
dc.date.issued2021
dc.departmentTarsus Üniversitesi
dc.description.abstractForecasting future driving conditions such as acceleration, velocity, and driver behaviors can greatly contribute to safety, mobility, and sustainability issues in the development of new energy vehicles (NEVs). In this brief, a review of existing velocity prediction techniques is studied from the perspective of traffic flow and vehicle lateral dynamics for the first time. A classification framework for velocity prediction in NEVs is presented where various state-of-the-art approaches are put forward. Firstly, we investigate road traffic flow models, under which a driving-scenario-based assessment is introduced. Secondly, vehicle speed prediction methods for NEVs are given where an extensive discussion on traffic flow model classification based on traffic big data and artificial intelligence is carried out. Thirdly, the influence of vehicle lateral dynamics and correlation control methods for vehicle speed prediction are reviewed. Suitable applications of each approach are presented according to their characteristics. Future trends and questions in the development of NEVs from different angles are discussed. Finally, different from existing review papers, we introduce application examples, demonstrating the potential applications of the highlighted concepts in next-generation intelligent transportation systems. To sum up, this review not only gives the first comprehensive analysis and review of road traffic network, vehicle handling stability, and velocity prediction strategies, but also indicates possible applications of each method to prospective designers, where researchers and scholars can better choose the right method on velocity prediction in the development of NEVs.
dc.description.sponsorshipNortheast Forestry University [520-60201418]
dc.description.sponsorshipThis research was funded by Northeast Forestry University No. 520-60201418.
dc.identifier.doi10.3390/en14123431
dc.identifier.issn1996-1073
dc.identifier.issue12
dc.identifier.scopus2-s2.0-85108640087
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/en14123431
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1590
dc.identifier.volume14
dc.identifier.wosWOS:000666073900001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofEnergies
dc.relation.publicationcategoryDiğer
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250316
dc.subjectnew energy vehicles
dc.subjectspeed prediction
dc.subjectmacroscopic traffic model
dc.subjecttraffic big-data
dc.subjectdeep learning
dc.subjectvehicle lateral dynamic and control
dc.subjectunresolved issues
dc.subjectapplication of speed prediction
dc.titleVelocity Prediction Based on Vehicle Lateral Risk Assessment and Traffic Flow: A Brief Review and Application Examples
dc.typeReview

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