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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Lin Li & Serdar Coskun & Jiaze Wang & Youming Fan & Fengqi Zhang & Reza Langari, 2021. "Velocity Prediction Based on Vehicle Lateral Risk Assessment and Traffic Flow: A Brief Review and Application Examples," Energies, MDPI, vol. 14(12), pages 1-30, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3431-:d:572474
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    Cited by:

    1. Francis F. Assadian, 2022. "Advanced Control and Estimation Concepts and New Hardware Topologies for Future Mobility," Energies, MDPI, vol. 15(4), pages 1-3, February.
    2. Qigang Zhu & Yifan Liu & Ming Liu & Shuaishuai Zhang & Guangyang Chen & Hao Meng, 2021. "Intelligent Planning and Research on Urban Traffic Congestion," Future Internet, MDPI, vol. 13(11), pages 1-17, November.

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