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

Lin Li, Serdar Coskun, Jiaze Wang, Youming Fan, Fengqi Zhang and Reza Langari
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Lin Li: School of Traffic and Transportation, Northeast Forestry University, Harbin 150040, China
Serdar Coskun: Department of Mechanical Engineering, Tarsus University, Tarsus, Mersin 33400, Turkey
Jiaze Wang: School of Traffic and Transportation, Northeast Forestry University, Harbin 150040, China
Youming Fan: School of Traffic and Transportation, Northeast Forestry University, Harbin 150040, China
Fengqi Zhang: School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China
Reza Langari: Department of Mechanical Engineering, Texas A&M University, College Station, TX 77840, USA

Energies, 2021, vol. 14, issue 12, 1-30

Abstract: Forecasting 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.

Keywords: new energy vehicles; speed prediction; macroscopic traffic model; traffic big-data; deep learning; vehicle lateral dynamic and control; unresolved issues; application of speed prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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