Safety distance model for longitudinal collision avoidance of logistics vehicles considering slope and road adhesion coefficient

With the acceleration of urbanization process, the country’s strong support for the healthy development of the logistics industry has made urban logistics become a hot topic in recent years. With the increase in the number of logistics vehicles, traffic accidents have become more frequent. Intelligent vehicle collision avoidance system is an important part of advanced safety technology. To increase veracity and practicability of logistics vehicle safety collision avoidance, this paper presents a safety distance model for longitudinal collision avoidance of logistics vehicles considering road slope and road adhesion coefficient. Based on the vehicle kinetic theory, the information of surrounding environment for the vehicle is obtained using environment sensing system adequately, a method is designed to estimate the road slope and road adhesion coefficient. Combined with vehicle dynamics and tire normal force variation, the road slope was estimated. Based on the relationship between slip rate and adhesion coefficient, the Least Square Method is used for multivariate fitting to obtain the relationship between rolling resistance coefficient and road adhesion coefficient, estimate the road adhesion coefficient, and the maximum deceleration of vehicle braking is modified. Therefore, the safety distance model is established. In order to verify the accuracy and effectiveness of the model, three cases are designed for verification: case I is verification of the safety distance model considering the slope factor; case II is verification of the safety distance model considering the road adhesion factor; case III is the safety distance verification considering both the factors of slope and road adhesion coefficient. The result shows that it is necessary to take the factors of road slope and adhesion coefficient into the safety distance model to improve the accuracy of the model.

Language

  • English

Media Info

Subject/Index Terms

Filing Info

  • Accession Number: 01765324
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jan 21 2021 3:26PM