Rollover risk assessment and automated control for heavy duty vehicles based on vehicle-to-infrastructure information

A novel rollover risk assessment and control approach is presented. First, a road-vehicle-environment coupling model with various random parameters, including wind velocity and road curvature, is developed. Second, a safety margin is defined to divide the safe and dangerous domains in the parameter space. Then, the first-order reliability method (FORM) approximation is developed to evaluate the probability of rollover accidents using a vehicle dynamics model. Finally, the state-space equation of automated control system is built based on model prediction control (MPC). With the front wheel steering angle and four wheels' braking torque as the control inputs, the additional yaw moment is used to prevent vehicle rollover. The control system model is implemented on the Trucksim/Simulink simulation platform. The results show that the automated control system proposed can prevent vehicle rollover effectively and enhance the driving performance of the vehicle. This study suggests that the presented rollover risk assessment and control methodologies can effectively estimate the rollover risk for HDVs under complex environments, whereas doing so would be very difficult with sensors alone.