Adaptive Cost Function-Based Shared Driving Control for Cooperative Lane-Keeping Systems With User-Test Experiments

This article presents a new shared control method with a dynamic driver-automation conflict management for cooperative lane keeping systems (LKS) of automated vehicles. Based on a human-centered control approach, the proposed shared control design takes into account the driver's activity and availability, as well as the surrounding risk to
dynamically
adapt the driving assistance level via an adaptive cost function. To this end, we propose a method to characterize in real-time the driver's activity, which allows for an appropriate assistance level according to the driving conditions. Linear parameter-varying (LPV) control technique is leveraged to deal with the time-varying nature of the vehicle speed and the level of assistance. The uncertainties of the lateral tires forces are taken into account in the control design via a norm-bounded representation. Using Lyapunov stability theory, the control design conditions are derived in terms of linear matrix inequality (LMI) constraints, which can be effectively solved by semidefinite programming techniques. User-test experiments are performed with the SHERPA dynamic car simulator to demonstrate the effectiveness of the proposed shared control method from both objective and subjective viewpoints.