Anisotropy safety potential field model under intelligent and connected vehicle environment and its application in car-following modeling

Potential field theory, as a theory that can also be applied to vehicle control, is an emerging risk quantification approach accommodate the connected and self-driving environment. Vehicles have different impact effects on other road participants in each direction under influence of rules. This variability exhibited by vehicles not considered previous potential model. Therefore, this paper proposed model takes anisotropy into account: (1) introducing equivalent distances separate area directions before after vehicle; (2) co-virtual forces characterize effect side-by-side travel phenomenon car-following travel; (3) target lane resistance, which regress control desired speed acceptable drivers. The Next Generation Simulation (NGSIM) dataset used study create model's initial parameter values based artificial swarm algorithm. simulation findings indicate when given capacity perceive surrounding traffic environment, suggested anisotropic safety (ASPFM) performs better terms driving safety.