A lateral control authority allocation strategy for lane-changing behavior for human-machine co-driving vehicles

In the context of parallel collaborative lane-changing control for drivers and intelligent systems, issues such as human-machine conflict and driving discomfort arise due to frequent or substantial changes in the authority of the front wheel steering angle input. This paper proposes a lateral human-machine driving authority allocation strategy that combines pre-allocation with real-time allocation to achieve a reasonable allocation of vehicle control authority. Initially, to characterize the driving style of the driver, a single-point preview driver model is constructed, which includes decision functions for the tracking error and the lateral acceleration. Concurrently, a model predictive controller (MPC) is established as a co-driving control system, and a vehicle lane-changing trajectory is designed based on a quintic polynomial. Subsequently, a pre-allocation method for driving authority is designed, incorporating style coefficients, preview time, and road adhesion coefficients. Principles for real-time authority allocation are designed based on risk level and human-machine conflict, with adjustments introduced to prevent frequent changes in authority. Joint simulation results indicate that when human-machine intentions are consistent, this strategy significantly reduces the driver’s burden. When driving risk is high, control authority shifts toward the system, allowing timely intervention to ensure driving safety. When human-machine intentions are inconsistent, and driving risk is low but human-machine conflict is high, authority is transferred to the driver at a fixed value, allowing the vehicle to operate according to the driver’s intention, and the overall control effect is superior to fixed authority control strategies. Driver-in-the-loop platform tests have shown that this strategy can provide personalized lane-changing assistance for drivers of different styles, demonstrating superior performance compared to both fuzzy control and human-machine game control approaches.