Game-Theoretic Shared Control Strategy for Cooperative Collision Avoidance Under Extreme Conditions

Driver-automation cooperative driving has recently been proved to be a promising approach to enhance driving safety in common or even relatively complex traffic scenarios. However, the potential of this cooperative driving system to enhance driving safety under extreme conditions remains underexplored. This paper presents a game-theoretic shared steering control (SSC) strategy, which takes nonlinear vehicle dynamics as well as state and input constraints into consideration, for driver-automation cooperative collision avoidance under extreme conditions. Firstly, a novel Takagi-Sugeno (T-S) fuzzy Koopman operator approach for vehicle system modeling is introduced. This approach ensures the accuracy of the Koopman-based vehicle model in the presence of changing external conditions, such as road adhesion coefficient and vertical load. The resulting vehicle model maintains a simple linear structure while preserving system nonlinearity. Then, based on the T-S fuzzy Koopman vehicle model, we design a game-theoretic SSC strategy that considers state and input constraints. The state constraints are further approximated through artificial potential field functions and second-order Taylor series expansions, enabling the derivation of analytical Nash equilibrium solution using the distributed model predictive control approach. Finally, the effectiveness of the proposed SSC strategy is validated through both numerical and driver-in-the-loop (DiL) experiments. The results indicate that the proposed SSC strategy significantly improves the vehicle's collision-avoidance performance while reducing the driver's workloads.