The Effect of Pervasive Computing and Driver’s Memory on Connected and Autonomous Vehicles

Abstract

Pervasive computing techniques provides application guidance and support for intelligent transportation systems. On the basis of connected autonomous vehicles, a extended model has been proposed. This paper focuses on studying the effects of the electronic throttle and driver’s memory. In order to show the influence of these two factors on the model, the extended model is studied by theoretical analysis and numerical methods. Stability conditions are obtained by adding perturbation to the linear analysis, the TDGL equation and mKdV equation are derived by nonlinear analysis, which shows that the phase change behavior of traffic jams can be described by both of them. The numerical simulation results show the correctness of the analysis results. Numerical results show that the effects of driver’s memory and electronic throttle play an key role in the stability of traffic flow, which is consistent with the theoretical analysis results.

Keywords

• TDGL equation
• mKdV equation
• driver's memory
• electronic throttle
• traffic flow