Real time identification of cornering coefficients and ideal twin driving assistance

Abstract

Cornering coefficients play a crucial role in vehicles’ lateral and longitudinal dynamics. They depend on many factors, including environmental factors such as road conditions. In this study, a method that identifies cornering coefficients in real-time by utilizing deep neural networks is developed. Three different neural network architectures with two different datasets are compared for this identification. Results show that a fully connected network trained with time-varying cornering coefficients performs best. Compared to constant cornering coefficients, this method improves the lateral force estimation between 42.62-75.47 % in experiments conducted on a 1/8 scale four-wheel drive four-wheel steering vehicle. A control method that utilizes the identified cornering coefficients to cancel out the changes in cornering coefficient by utilizing 4 wheel drive 4 wheel steering system is developed. The control method utilizes a nonlinear model predictive controller. The control system uses the driver’s control inputs in an ideal front wheel drive front wheel steering twin of the vehicle with constant cornering coefficients set by the driver, to obtain reference velocities. The nonlinear model predictive controller can calculate the optimal control inputs for a 4-wheel drive, 4-wheel steering vehicle from these references. The simulation results show that this control method improves reference tracking by 85 % compared to a conventional configuration, i.e., a front wheel drive front wheel steering vehicle. The controller is tested on an experimental setting and found to be improving the results by 12.12 %. The reduction in improvement can be attributed to noise in measurements and delays in the control system.